From 1ec77b58b4ccbf88fb06688dfd1e0cec793da0bf Mon Sep 17 00:00:00 2001
From: Chi Wang <wang.chi@microsoft.com>
Date: Sun, 5 Mar 2023 08:40:57 -0800
Subject: [PATCH] improve max_valid_n and doc (#933)

* improve max_valid_n and doc

* Update README.md

Co-authored-by: Li Jiang <lijiang1@microsoft.com>

* newline at end of file

* doc

---------

Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
---
 README.md                            |   10 +-
 flaml/automl/ml.py                   |    4 +-
 flaml/integrations/oai/completion.py |   38 +-
 notebook/automl_nlp.ipynb            | 2957 +++++++++++++-------------
 notebook/integrate_openai.ipynb      |  492 ++---
 setup.py                             |    9 +-
 website/docs/Examples/AutoML-NLP.md  |    4 +-
 website/docs/Getting-Started.md      |    6 +-
 website/docs/Installation.md         |   12 +-
 9 files changed, 1780 insertions(+), 1752 deletions(-)

diff --git a/README.md b/README.md
index 71362b023..80d7ef13c 100644
--- a/README.md
+++ b/README.md
@@ -14,20 +14,22 @@
     <br>
 </p>
 
-:fire: An [upcoming tutorial on FLAML](https://github.com/microsoft/FLAML/tree/tutorial-aaai23/tutorial) at [AAAI-23](https://aaai.org/Conferences/AAAI-23/aaai23tutorials/) (to be held on Feb 08, 2023)
+:fire: OpenAI GPT-3 models support in v1.1.3. ChatGPT support is coming.
+
+:fire: A [lab forum](https://github.com/microsoft/FLAML/tree/tutorial-aaai23/tutorial) on FLAML at AAAI 2023.
 
 :fire: A [hands-on tutorial](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) on FLAML presented at KDD 2022
 
 ## What is FLAML
 FLAML is a lightweight Python library that finds accurate machine
 learning models automatically, efficiently and economically. It frees users from selecting
-learners and hyperparameters for each learner. It can also be used to tune generic hyperparameters for MLOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
+models and hyperparameters for each model. It can also be used to tune generic hyperparameters for large language models (LLM), MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
 
-1. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classifcal machine learning models and deep neural networks.
+1. For common machine learning or AI tasks like classification, regression, and generation, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks, including large language models such as the OpenAI GPT-3 models.
 1. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
 1. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
 hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
-and learner selection method invented by Microsoft Research.
+and model selection method invented by Microsoft Research, and many followup [research studies](https://microsoft.github.io/FLAML/docs/Research).
 
 FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
 
diff --git a/flaml/automl/ml.py b/flaml/automl/ml.py
index 1bed9992a..55fcd932e 100644
--- a/flaml/automl/ml.py
+++ b/flaml/automl/ml.py
@@ -207,11 +207,11 @@ def metric_loss_score(
         except ImportError:
             raise ValueError(
                 metric_name
-                + " is not an built-in sklearn metric and nlp is not installed. "
+                + " is not an built-in sklearn metric and [hf] is not installed. "
                 "Currently built-in sklearn metrics are: "
                 "r2, rmse, mae, mse, accuracy, roc_auc, roc_auc_ovr, roc_auc_ovo,"
                 "log_loss, mape, f1, micro_f1, macro_f1, ap. "
-                "If the metric is an nlp metric, please pip install flaml[nlp] ",
+                "If the metric is a huggingface metric, please pip install flaml[hf] ",
                 "or pass a customized metric function to AutoML.fit(metric=func)",
             )
         # If the metric is not found from huggingface dataset metric list (i.e., FileNotFoundError)
diff --git a/flaml/integrations/oai/completion.py b/flaml/integrations/oai/completion.py
index 0d79e755b..001311e22 100644
--- a/flaml/integrations/oai/completion.py
+++ b/flaml/integrations/oai/completion.py
@@ -179,6 +179,7 @@ class Completion:
         """
         cost = 0
         data = cls.data
+        data_length = len(data)
         target_n_tokens = (
             1000 * cls.inference_budget / cls.price1K[config["model"]]
             if cls.inference_budget and cls.price1K.get(config["model"])
@@ -187,26 +188,33 @@ class Completion:
         prune_hp = cls._prune_hp
         metric = cls._metric
         config_n = config[prune_hp]
-        max_tokens = config["max_tokens"]
+        max_tokens = config.get("max_tokens", 16)  # default value in OpenAI is 16
         region_key = cls._get_region_key(config)
         prompt = cls._prompts[config["prompt"]]
         stop = cls._stops and cls._stops[config["stop"]]
         if prune and target_n_tokens:
             max_valid_n = cls._get_max_valid_n(region_key, max_tokens)
-            min_invalid_n = cls._get_min_invalid_n(region_key, max_tokens)
-            if min_invalid_n is not None and config_n >= min_invalid_n:
-                if config_n > max_valid_n:
+            if cls.avg_input_tokens:
+                # max_tokens bounds the maximum tokens
+                # so using it we can calculate a valid n according to the avg # input tokens
+                max_valid_n = max(
+                    max_valid_n,
+                    int((target_n_tokens - cls.avg_input_tokens) // max_tokens),
+                )
+            else:
+                input_tokens = [None] * data_length
+            if config_n <= max_valid_n:
+                start_n = config_n
+            else:
+                min_invalid_n = cls._get_min_invalid_n(region_key, max_tokens)
+                if min_invalid_n is not None and config_n >= min_invalid_n:
                     # prune this config
                     return {
                         "inference_cost": np.inf,
                         metric: np.inf if cls._mode == "min" else -np.inf,
                         "cost": cost,
                     }
-                # since config_n<=max_valid_n, there is a chance config_n is valid
-                start_n = config_n
-            else:
-                # start from a valid n
-                start_n = min(max_valid_n, config_n)
+                start_n = max_valid_n + 1
         else:
             start_n = config_n
         params = config.copy()
@@ -214,7 +222,6 @@ class Completion:
         temperature_or_top_p = params.pop("temperature_or_top_p", None)
         if temperature_or_top_p:
             params.update(temperature_or_top_p)
-        data_length = len(data)
         num_completions, previous_num_completions = start_n, 0
         n_tokens_list, result, responses_list = [], {}, []
         while True:  # n <= config_n
@@ -242,6 +249,14 @@ class Completion:
                         if previous_num_completions
                         else response["usage"]["total_tokens"]
                     )
+                    if (
+                        prune
+                        and target_n_tokens
+                        and not cls.avg_input_tokens
+                        and not input_tokens[i]
+                    ):
+                        # store the # input tokens
+                        input_tokens[i] = response["usage"]["prompt_tokens"]
                     # Under Assumption 1, we should count both the input and output tokens in the first query,
                     # and only count ouput tokens afterwards
                     query_cost = (
@@ -335,6 +350,8 @@ class Completion:
                 result["inference_cost"] = (
                     avg_n_tokens * cls.price1K[config["model"]] / 1000
                 )
+                if prune and target_n_tokens and not cls.avg_input_tokens:
+                    cls.avg_input_tokens = np.mean(input_tokens)
                 break
             else:
                 if data_early_stop:
@@ -424,6 +441,7 @@ class Completion:
             cls._total_cost = 0  # total optimization cost
             cls._eval_func = eval_func
             cls.data = data
+            cls.avg_input_tokens = None
 
             search_alg = BlendSearch(
                 cost_attr="cost",
diff --git a/notebook/automl_nlp.ipynb b/notebook/automl_nlp.ipynb
index b6c83cb19..9b5c566c6 100644
--- a/notebook/automl_nlp.ipynb
+++ b/notebook/automl_nlp.ipynb
@@ -3,14 +3,15 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/liususan091219/FLAML-pub/blob/main/notebook/automl_nlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "43f7-wG-Tjg_"
@@ -28,7 +29,7 @@
         "- be embedded in self-tuning software that requires low latency & resource in repetitive\n",
         "   tuning tasks.\n",
         "\n",
-        "In this notebook, we demonstrate how to use the FLAML library to fine tune an NLP language model with hyperparameter search. We will use [flaml.tune](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function) with the built in GPU in colab for the tuning. However, if you have a machine with more than 1 GPU, you can also use FLAML's [parallel tuning](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) with the ray tune option. \n",
+        "In this notebook, we demonstrate how to use the FLAML library to fine tune an huggingface language model with hyperparameter search. We will use [flaml.automl](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML) with the built in GPU in colab for the tuning. However, if you have a machine with more than 1 GPU, you can also use FLAML's [parallel tuning](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) with the ray tune option. \n",
         "\n",
         "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `nlp,notebook` and `blendsearch` option:\n",
         "```bash\n",
@@ -48,8 +49,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Requirement already satisfied: flaml[blendsearch,nlp,notebook] in /usr/local/lib/python3.7/dist-packages (1.0.11)\n",
@@ -84,8 +85,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "[<torch.cuda.device object at 0x7fa1e4115190>]\n"
           ]
@@ -176,146 +177,146 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/json": {
+              "ascii": false,
+              "bar_format": null,
+              "colour": null,
+              "elapsed": 0.023516416549682617,
+              "initial": 0,
+              "n": 0,
+              "ncols": null,
+              "nrows": null,
+              "postfix": null,
+              "prefix": "Downloading data",
+              "rate": null,
+              "total": 7439277,
+              "unit": "B",
+              "unit_divisor": 1000,
+              "unit_scale": true
+            },
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "16fba9eb9e4542bc9d34eca00d71cc14",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Downloading data:   0%|          | 0.00/7.44M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "16fba9eb9e4542bc9d34eca00d71cc14"
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-            "application/json": {
-              "n": 0,
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-              "elapsed": 0.023516416549682617,
-              "ncols": null,
-              "nrows": null,
-              "prefix": "Downloading data",
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-              "unit": "B",
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-              "rate": null,
-              "bar_format": null,
-              "postfix": null,
-              "unit_divisor": 1000,
-              "initial": 0,
-              "colour": null
-            }
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/json": {
+              "ascii": false,
+              "bar_format": null,
+              "colour": null,
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+              "initial": 0,
+              "n": 0,
+              "ncols": null,
+              "nrows": null,
+              "postfix": null,
+              "prefix": "Generating train split",
+              "rate": null,
+              "total": 67349,
+              "unit": " examples",
+              "unit_divisor": 1000,
+              "unit_scale": false
+            },
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2aa02213244048fead33ea157c17837b",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Generating train split:   0%|          | 0/67349 [00:00<?, ? examples/s]"
-            ],
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+          "metadata": {},
+          "output_type": "display_data"
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+              "unit": " examples",
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+              "prefix": "Generating test split",
+              "rate": null,
+              "total": 1821,
+              "unit": " examples",
+              "unit_divisor": 1000,
+              "unit_scale": false
+            },
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2c31d2eb9ae44ffbb0f02ad1b1e7937a",
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+              "version_minor": 0
+            },
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               "Generating test split:   0%|          | 0/1821 [00:00<?, ? examples/s]"
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+            ]
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+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "WARNING:datasets.builder:Reusing dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n",
             "WARNING:datasets.builder:Reusing dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
@@ -530,8 +531,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
             "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -556,8 +557,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5665, 'learning_rate': 4.6751863684771026e-06, 'epoch': 1.6}\n",
             "{'eval_loss': 0.42372775077819824, 'eval_automl_metric': 0.1754587155963303, 'eval_runtime': 10.818, 'eval_samples_per_second': 80.606, 'eval_steps_per_second': 80.606, 'epoch': 2.0}\n",
@@ -566,8 +567,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -589,8 +590,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'eval_loss': 0.4843567907810211, 'eval_automl_metric': 0.18233944954128445, 'eval_runtime': 10.5457, 'eval_samples_per_second': 82.688, 'eval_steps_per_second': 82.688, 'epoch': 2.0}\n",
             "{'eval_loss': 0.4618026912212372, 'eval_automl_metric': 0.17889908256880738, 'eval_runtime': 10.645, 'eval_samples_per_second': 81.916, 'eval_steps_per_second': 81.916, 'epoch': 3.0}\n",
@@ -598,8 +599,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -619,8 +620,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5778, 'learning_rate': 7.550901222797876e-06, 'epoch': 0.8}\n",
             "{'loss': 0.3836, 'learning_rate': 4.805118959962285e-06, 'epoch': 1.6}\n",
@@ -631,8 +632,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -654,8 +655,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5362, 'learning_rate': 8.879996750213199e-06, 'epoch': 0.8}\n",
             "{'eval_loss': 0.3863365948200226, 'eval_automl_metric': 0.1594036697247706, 'eval_runtime': 10.6255, 'eval_samples_per_second': 82.067, 'eval_steps_per_second': 82.067, 'epoch': 1.0}\n",
@@ -665,8 +666,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -686,8 +687,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.6402, 'learning_rate': 5.730904302906456e-06, 'epoch': 0.8}\n",
             "{'loss': 0.4537, 'learning_rate': 4.298178227179842e-06, 'epoch': 1.6}\n",
@@ -698,8 +699,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -719,8 +720,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5223, 'learning_rate': 1.3121346786922505e-05, 'epoch': 0.8}\n",
             "{'loss': 0.333, 'learning_rate': 8.349947955314322e-06, 'epoch': 1.6}\n",
@@ -731,8 +732,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -752,8 +753,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.6593, 'learning_rate': 4.3452939856201385e-06, 'epoch': 0.8}\n",
             "{'loss': 0.5039, 'learning_rate': 2.76518708175827e-06, 'epoch': 1.6}\n",
@@ -764,8 +765,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -785,8 +786,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.4949, 'learning_rate': 1.624682269684853e-05, 'epoch': 0.8}\n",
             "{'loss': 0.3234, 'learning_rate': 1.0338887170721792e-05, 'epoch': 1.6}\n",
@@ -797,8 +798,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -820,8 +821,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5516, 'learning_rate': 2.1517120038185796e-05, 'epoch': 0.4}\n",
             "{'loss': 0.3741, 'learning_rate': 1.8206793878464904e-05, 'epoch': 0.8}\n",
@@ -831,8 +832,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -852,8 +853,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.4588, 'learning_rate': 9.24274365870653e-06, 'epoch': 1.6}\n",
             "{'eval_loss': 0.35305526852607727, 'eval_automl_metric': 0.14220183486238536, 'eval_runtime': 10.7298, 'eval_samples_per_second': 81.269, 'eval_steps_per_second': 81.269, 'epoch': 2.0}\n",
@@ -863,8 +864,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -884,8 +885,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.5476, 'learning_rate': 1.0987792912546241e-05, 'epoch': 0.8}\n",
             "{'eval_loss': 0.41232776641845703, 'eval_automl_metric': 0.1594036697247706, 'eval_runtime': 10.5607, 'eval_samples_per_second': 82.57, 'eval_steps_per_second': 82.57, 'epoch': 1.35}\n",
@@ -894,8 +895,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -915,8 +916,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.4679, 'learning_rate': 2.402295436797273e-05, 'epoch': 0.8}\n",
             "{'eval_loss': 0.3937930166721344, 'eval_automl_metric': 0.1513761467889908, 'eval_runtime': 10.5403, 'eval_samples_per_second': 82.73, 'eval_steps_per_second': 82.73, 'epoch': 1.14}\n",
@@ -925,8 +926,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -946,8 +947,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'loss': 0.4939, 'learning_rate': 2.1277689409714175e-05, 'epoch': 0.12}\n",
             "{'loss': 0.3558, 'learning_rate': 2.040062059645308e-05, 'epoch': 0.24}\n",
@@ -971,8 +972,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -1024,8 +1025,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "The best loss by FLAML: 0.9105504587155964\n"
           ]
@@ -1074,8 +1075,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Best hyperparmeter config: {'learning_rate': 2.215475822297527e-05, 'num_train_epochs': 3, 'per_device_train_batch_size': 16, 'seed': 24, 'global_max_steps': 8112, 'FLAML_sample_size': 67349}\n",
             "Best accuracy on validation data: 0.9106\n",
@@ -1092,27 +1093,27 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Save and load the model:"
-      ],
       "metadata": {
         "id": "MqIpmxl0dKWu"
-      }
+      },
+      "source": [
+        "Save and load the model:"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "gfUNXfcNTBA2"
+      },
+      "outputs": [],
       "source": [
         "import pickle\n",
         "automl.pickle(\"automl.pkl\")\n",
         "\n",
         "with open(\"automl.pkl\", \"rb\") as f:\n",
         "    automl = pickle.load(f)"
-      ],
-      "metadata": {
-        "id": "gfUNXfcNTBA2"
-      },
-      "execution_count": 27,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -1138,8 +1139,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "***** Running Prediction *****\n",
             "  Num examples = 872\n",
@@ -1153,8 +1154,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Predicted labels [1 1 1 1 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1\n",
             " 0 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 1 1 1 0 0 1 1 1\n",
@@ -1219,8 +1220,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 9.999999999999999e-06, 'num_train_epochs': 3, 'per_device_train_batch_size': 32, 'seed': 20, 'global_max_steps': 939, 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 9.999999999999999e-06, 'num_train_epochs': 3, 'per_device_train_batch_size': 32, 'seed': 20, 'global_max_steps': 939, 'FLAML_sample_size': 10000}}\n",
             "{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 9.711865003865157e-06, 'num_train_epochs': 3, 'per_device_train_batch_size': 64, 'seed': 14, 'global_max_steps': 471, 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 9.999999999999999e-06, 'num_train_epochs': 3, 'per_device_train_batch_size': 32, 'seed': 20, 'global_max_steps': 939, 'FLAML_sample_size': 10000}}\n",
@@ -1259,23 +1260,23 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "13\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -1828,7 +1829,7 @@
         },
         {
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -2276,7 +2277,7 @@
         },
         {
           "data": {
-            "image/png": 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\n",
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1ifmLO5l981K6Nm0BoHNDF7NvXgpA25TtFhzfKUWuqnqDpNGVHUljKJZwzMysn81pX7k1aVR0bdrCnPaVffYeRRLAPwP3Srop7X8SuLjPIjAzsz6zdkNXqfKdUWRy/BpJHUDlAUonRsSKPovAzMz6zLimRjq7SRLjmhr77D2KTI6/C3gyIv41Iv6V7ImA7+yzCMzMrM/MnDqJxhEN25Q1jmhg5tQdPvG7sCJzHN8Afpfb/10qMzOzOtM2pYVLTjyclqZGBLQ0NXLJiYf32cQ4FJvjUHo2OAAR8ZokT46bmdWptiktfZooqhXpcayW9AVJI9Lrr4DVNYvIzMzqWpHEcRbwHqATWAO8EzijSOOSpklaKWmVpFk91DlZ0gpJyyVdlyvfImlJei3IlU+QdH9q80ZJI4vEYmZmfaPIVVVPATMq+5IagY8CN/V4UlavAZgLHEeWcBZJWpC/IkvSRGA2cHS6sXDfXBNdETG5m6a/ClwWETdIuhz4MzznYmbWbwotqy6pQdJHJH0HeAz4VIHTjgJWRcTqiNgI3MD2iyOeAcyNiOdga5LqLQ6RXRY8LxV9G2gr8hnMzKxv9Jo4JL1f0v8nWxH3z8h6D2+JiJMKtN0CPJnbX5PK8g4GDpZ0t6T7JE3LHdtDUkcqb0tlewMbImJzL21WYj8znd+xfv36AuGamVkRPQ5VSVoDPEE2DPQ3EfGipMci4uU+fv+JwAeA/YCfSjo8rY11YER0SnoLcIekpcDzRRuOiCuAKwBaW1tjB9XNzKyg3noc84BxZMNSH5P0Rl5/9ngRncD+uf39UlneGmBBRGyKiMeAh8kSCRHRmX6uBn4CTAGeAZpylwN316aZmdVQj4kjIv4amEC2VtUHgJVAc7oK6vcKtL0ImJiughpJNsG+oKrO/NQ2kvYhG7panZZu3z1XfjSwIt1PcidQGSo7Fbi1QCxmZtZHep3jSM8YvzMiziRLIqeQTXA/vqOG0zzE2UA78EvgexGxXNKFkk5I1dqBZyStIEsIMyPiGeCtQIekX6TyS3NXY50LnCNpFdmcx7+X+sRmZrZLCt8BHhGbgB8AP0iX5BY5ZyGwsKrs/Nx2AOekV77OPcDhPbS5muyKLTMzGwCFLsetFhF9tz6vmZkNKjuVOMzMbPhy4jAzs1J2OMch6WBgJnBgvn5EHNvjSWZmNmQVmRy/CbgcuBLYsoO6Zma2A/MXdzKnfSVrN3QxrqmRmVMn1XQZ9L5WJHFsjggvImhm1gfmL+5k9s1L6dqUfQ/v3NDF7JuXAgya5FFkjuP7kj4naaykMZVXzSMzMxuC5rSv3Jo0Kro2bWFO+8oBiqi8Ij2OU9PPmbmyAN7S9+GYmQ1tazd0fzdDT+X1qMjzOCb0RyBmZsPBuKZGOrtJEuOaCt1XXRd2OFSVHhf7BUnz0utsSSP6Izgzs6Fm5tRJNI5o2KascUQDM6dOGqCIyisyVPUNYATwb2n/T1PZZ2sVlJnZUFWZAB/qV1W9IyL+ILd/R1p80MzMdkLblJZBlSiqFbmqaouk/1XZSQ9W8v0cZmbDVJEex0zgTkmrAZHdQX56TaMyM7O6VeSqqv+SNBGozNysjIhXaxuWmZnVq96eOX5sRNwh6cSqQwdJIiJurnFsZmZWh3rrcbwfuAP4WDfHAnDiMDMbhnpMHBHxlbR5YUQ8lj8myTcFmpkNU0Umx/8DOKKqbB5wZN+HMzwM9pUxzWx4622O4xDgMGCvqnmOUcAetQ5sqBoKK2Oa2fDW230ck4CPAk1k8xyV1xHAGUUalzRN0kpJqyTN6qHOyZJWSFou6bpUNlnSvansIUmfytW/WtJjkpak1+QisdSLobAyppkNb73NcdwK3Crp3RFxb9mGJTUAc4HjgDXAIkkLImJFrs5EYDZwdEQ8J2nfdOhl4NMR8YikccCDktojYkM6PjMi5pWNqR4MhZUxzWx4KzLHsVjSX5INW20dooqIz+zgvKOAVRGxGkDSDcB0YEWuzhnA3Ih4LrX5VPr5cO591kp6CmgGNhSIt64NhZUxzWx4K7LkyHeA3wemAncB+wEvFjivBXgyt78mleUdDBws6W5J90maVt2IpKOAkcCjueKL0xDWZZJ27+7NJZ0pqUNSx/r16wuE2z+GwsqYZja8FUkcB0XEl4GXIuLbwP8G3tlH778bMBH4AHAKcKWkpspBSWPJEtfpEfFaKp4NHAK8AxgDnNtdwxFxRUS0RkRrc3NzH4W769qmtHDJiYfT0tSIgJamRi458XBPjJvZoFFkqGpT+rlB0tuA3wD79lK/ohPYP7e/XyrLWwPcHxGbgMckPUyWSBZJGgX8EDgvIu6rnBAR69Lmq5K+BfxNgVjqymBfGdPMhrciPY4rJI0GvgwsIJuj+McC5y0CJkqaIGkkMCOdnzefrLeBpH3Ihq5Wp/q3ANdUT4KnXgiSBLQBywrEYmZmfaTIIoffTJt3UeI54xGxWdLZQDvQAFwVEcslXQh0RMSCdOx4SSvIlmqfGRHPSPoT4H3A3pJOS02eFhFLgGslNZOt1LsEOKtoTGX4Jj0zs+71dgPgOb2dGBH/sqPGI2IhsLCq7PzcdgDnpFe+zneB7/bQ5rE7et9d5Zv0zMx61luP403p5ySyiejKMNPHgAdqGdRA6+kmvS/Ne4jrH3iCFete4NCxowYoOjOzgdXbDYB/DyDpp8AREfFi2r+AbNJ6yOrpZryNW7ILuw4dO4rpk93zMLPhqchVVW8GNub2N6ayIaunm/Ramhq58c/fPQARmZnVjyJXVV0DPCDpgtTbuB+4upZBDTTfpGdm1rMiV1VdLOk24L2p6PSIWFzbsAZWZQLcV1WZmW2vt6uqRkXEC5LGAI+nV+XYmIh4tvbhDRzfpGdm1r3eehzXkS2r/iDZo2IrlPYL39NhZmZDR29XVX00/fRjYs3MbKvehqqqHxe7jYj4ed+HY2Zm9a63oap/7uVYADW/g9vMzOpPb0NVx/RnIGZmNjgUuQGQtJz6oWz7BMBrahWUmZnVrx0mDklfIVv6/FCyBQs/DPw32Y2BZmY2zBS5c/wk4IPAbyLidOAPgL1qGpWZmdWtIomjKz22dXN6Kt9TbPtkPzMzG0aKzHF0pOeAX0l2M+DvgHtrGZSZmdWv3u7jmAtcFxGfS0WXS/oRMCoiHuqX6MzMrO701uN4GPin9Izv7wHXD/XFDc3MbMd6nOOIiK9FxLuB9wPPAFdJ+pWkr0g6uN8iNDOzurLDyfGI+HVEfDUipgCnAG3AL2sdmJmZ1acdJg5Ju0n6mKRrgduAlcCJRRqXNE3SSkmrJM3qoc7JklZIWi7pulz5qZIeSa9Tc+VHSlqa2vy6JBWJxczM+kZvk+PHkfUwPgI8ANwAnBkRLxVpWFIDMBc4DlgDLJK0ICJW5OpMBGYDR0fEc5L2TeVjgK8ArWTrYj2Yzn0O+AZwBtmTCBcC08gSmpmZ9YPeehyzgXuAt0bECRFxXdGkkRwFrIqI1RGxkSzxTK+qcwYwNyUEIuKpVD4VuD0ink3HbgempYn6URFxX0QE2d3rbSViMjOzXdTbIoe7uvptC/Bkbn8N8M6qOgcDSLobaAAuiIgf9XBuS3qt6aZ8O5LOBM4EOOCAA3b6Q5iZ2baK3DleS7sBE8nWwjoFuDLdbLjLIuKKiGiNiNbm5ua+aNLMzKht4uhk26VJ9ktleWuABRGxKSIeI7t3ZGIv53am7d7aNDOzGqpl4lgETJQ0QdJIYAawoKrOfLLeBpL2IRu6Wg20A8dLGi1pNHA80B4R64AXJL0rXU31aeDWGn4GMzOrUuh5HDsjIjZLOpssCTQAV0XEckkXAh0RsYDXE8QKYAswMyKeAZB0EVnyAbgwIp5N258DrgYaya6m8hVVZmb9SNnFSUNba2trdHR0DHQYZmaDiqQHI6K1unygJ8fNzGyQceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrxYnDzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrBQnDjMzK6WmiUPSNEkrJa2SNKub46dJWi9pSXp9NpUfkytbIukVSW3p2NWSHssdm1zLz2BmZtvarVYNS2oA5gLHAWuARZIWRMSKqqo3RsTZ+YKIuBOYnNoZA6wCfpyrMjMi5tUqdjMz61ktexxHAasiYnVEbARuAKbvRDsnAbdFxMt9Gp2Zme2UWiaOFuDJ3P6aVFbtE5IekjRP0v7dHJ8BXF9VdnE65zJJu/dRvGZmVsBAT45/HxgfEW8Hbge+nT8oaSxwONCeK54NHAK8AxgDnNtdw5LOlNQhqWP9+vW1iN3MbFiqZeLoBPI9iP1S2VYR8UxEvJp2vwkcWdXGycAtEbEpd866yLwKfItsSGw7EXFFRLRGRGtzc/MufhQzM6uoZeJYBEyUNEHSSLIhpwX5CqlHUXEC8MuqNk6hapiqco4kAW3Asr4N28zMelOzq6oiYrOks8mGmRqAqyJiuaQLgY6IWAB8QdIJwGbgWeC0yvmSxpP1WO6qavpaSc2AgCXAWbX6DGZmtj1FxEDHUHOtra3R0dEx0GGYmQ0qkh6MiNbq8oGeHDczs0HGicPMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrxYnDzMxKqdlaVcPB/MWdzGlfydoNXYxramTm1Em0TenukSNmZkOHE8dOmr+4k9k3L6Vr0xYAOjd0MfvmpQBOHmY2pHmoaifNaV+5NWlUdG3awpz2lQMUkZlZ/3Di2ElrN3SVKjczGyqcOHbSuKbGUuVmZkOFE8dOmjl1Eo0jGrYpaxzRwMypkwYoIjOz/uHJ8Z1UmQD3VVVmNtw4ceyCtiktThRmNux4qMrMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSlFEDHQMNSdpPfDrgY6jgH2Apwc6iAIcZ99ynH1vsMRa73EeGBHN1YXDInEMFpI6IqJ1oOPYEcfZtxxn3xsssQ6WOKt5qMrMzEpx4jAzs1KcOOrLFQMdQEGOs285zr43WGIdLHFuw3McZmZWinscZmZWihOHmZmV4sTRTyRNkrQk93pB0l9LGiPpdkmPpJ+jU31J+rqkVZIeknREP8b6RUnLJS2TdL2kPSRNkHR/iudGSSNT3d3T/qp0fHw/xvlXKcblkv46ldXF71PSVZKekrQsV1Y6NkmnpvqPSDq1n+L8ZPqdviaptar+7BTnSklTc+XTUtkqSbP6Kc45kn6Vfme3SGqq0zgvSjEukfRjSeNS+YD9ue+yiPCrn19AA/Ab4EDgH4FZqXwW8NW0/RHgNkDAu4D7+ym2FuAxoDHtfw84Lf2ckcouB/4ibX8OuDxtzwBu7Kc43wYsA/YkezzAfwIH1cvvE3gfcASwLFdWKjZgDLA6/Rydtkf3Q5xvBSYBPwFac+WHAr8AdgcmAI+mv8sNafstwMhU59B+iPN4YLe0/dXc77Pe4hyV2/5C7t/LgP257+rLPY6B8UHg0Yj4NTAd+HYq/zbQlranA9dE5j6gSdLYfopvN6BR0m5k/zGvA44F5vUQZyX+ecAHJakfYnwr2T+0lyNiM3AXcCJ18vuMiJ8Cz1YVl41tKnB7RDwbEc8BtwPTah1nRPwyIlZ2U306cENEvBoRjwGrgKPSa1VErI6IjcANqW6t4/xx+rMHuA/Yr07jfCG3+0agckXSgP257yonjoExA7g+bb85Ital7d8Ab07bLcCTuXPWpLKaiohO4J+AJ8gSxvPAg8CG3D/SfCxb40zHnwf2rnWcZL2N90raW9KeZN/e9qfOfp9VysZWDzHn1XOcnyH79k4v8QxYnJIulvQk8MfA+fUaZ1FOHP0szQ2cANxUfSyyfuqAXh+dxt2nk3Xxx5F9Q6qrbzuQfSsmG574MfAjYAmwparOgP8+e1LPsQ02ks4DNgPXDnQsPYmI8yJif7IYzx7oeHaVE0f/+zDw84j4bdr/bWXIJP18KpV3kn2DrtgvldXah4DHImJ9RGwCbgaOJutGVx41nI9la5zp+F7AM/0QJxHx7xFxZES8D3gOeJj6+33mlY2tHmLOq7s4JZ0GfBT445SM6SWeevh9Xgt8Im3Xc5y9cuLof6fw+jAVwAKgctXEqcCtufJPpysv3gU8nxvmqKUngHdJ2jPNVXwQWAHcCZzUQ5yV+E8C7sj9A64pSfumnweQzW9cR/39PvPKxtYOHC9pdOoJHp/KBsoCYIayK+kmABOBB4BFwERlV96NJBuKXVDrYCRNA74EnBARL9dxnBNzu9OBX+XiHAx/7tsb6Nn54fQiG/Z5BtgrV7Y38F/AI2RXBo1J5QLmkl0FspTc1S39EOffk/3lXgZ8h+zqlLeQ/eNbRTbMtnuqu0faX5WOv6Uf4/wZWVL7BfDBevp9kn05WAdsIhuj/rOdiY1s7H5Vep3eT3F+PG2/CvwWaM/VPy/FuRL4cK78I2Q9vkeB8/opzlVkcwFL0uvyOo3zP9K/pYeA7wMtA/3nvqsvLzliZmaleKjKzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4rBBT9JlSqvjpv12Sd/M7f+zpHN6Of9qSSel7Z9UrwibykdIujStVvpzSfdK+nA69rikfXYi7q3v28PxuWlF1RWSuvT6ysonSVqYXw22r0gaK+kHvRwfKemnuZtBbRhy4rCh4G7gPQCS3gDsAxyWO/4e4J5dfI+LgLHA2yLiCLIFCt+0i232KiL+MiImk9178GhETE6veRHxkYjYUIO3PQe4speYNpLdi/KpGry3DRJOHDYU3AO8O20fRnaz1YvpztvdyVbS/bmk8yUtUvYMjyuKruKbFlE8A/h8RLwKEBG/jYjvdVP3nNT+sqpe0KfTMxd+Iek73Zx3UeqBNBSM6XFJ+0gar+yZFFdLeljStZI+JOnu1Ds6KtV/o7JnRTwgabGknlaF/QTZ2l9IOizVX5Jir9wBPZ9ssT4bptzdtEEvItZK2pyWHnkPcC/ZaqLvJlutd2lEbJT0rxFxIUD6z/ujZHfy7shBwBOx7fLY25F0JHA68E6yu4Lvl3QXsBH4O+A9EfG0pDFV580h672cHjt3R+5BwCfJ7jZeBPwR8Idki2n+LVnv6Dyy5WA+k4a4HpD0nxHxUi6OCcBzleQInAV8LSKuTUt0VJLaMuAdOxGnDRHucdhQcQ9Z0qgkjntz+3enOscoe0rhUrLnixzWXUO74A+BWyLipYj4HdkCke9N73VTRDwNEBH55zV8mWwJmrN2MmlAtijl0oh4DVgO/FdqaykwPtU5HpglaQnZA5r2AA6oamcssD63fy/wt5LOBQ6MiK4U/xZgo6SaDtVZ/XLisKGiMs9xONk34vvIehzvAe6RtAfwb8BJEXE42Tj+HgXbXgUcIGlUn0ed9RCOrO6FlPRqbvu13P5rvD6qIOATuXmSAyJbmj6vi9zvJCKuI+u1dAELJR2bq7s78MouxGyDmBOHDRX3kA09PRsRW9K3+iay5HEPr/+H+LSk3+P1lX53KLKVV/8d+Jpef9Z6s6RPVlX9GdCmbGXhN5ItFvgz4A7gk5L2Tufmk8SPgEuBH9b4G3w78PnKvI6kKd3UeZjXeyhIeguwOiK+TraS79tT+d7A05Etu2/DkBOHDRVLya6muq+q7PmIeDpdgXQlWW+kneybfhl/RzaMs0LSMuAHwDZzHhHxc+BqslWC7we+GRGLI2I5cDFwl6RfAP9Sdd5NKbYFkhpLxlXURcAI4CFJy9P+NtJ8x6OSDkpFJwPL0vDW24BrUvkxwA9rFKcNAl4d18y2kvRx4MiI+Lte6twMzIqIh/svMqsnvqrKzLaKiFsqQ2rdSUN18500hjf3OMzMrBTPcZiZWSlOHGZmVooTh5mZleLEYWZmpThxmJlZKf8DKMTcjGm+IEYAAAAASUVORK5CYII=",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -2614,7 +2615,7 @@
         },
         {
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -2651,18 +2652,16 @@
         "id": "AzID7DyALObP"
       },
       "outputs": [],
-      "source": [
-        ""
-      ]
+      "source": []
     }
   ],
   "metadata": {
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+     "iopub.status.idle": "2023-02-24T23:25:39.176343Z",
+     "shell.execute_reply": "2023-02-24T23:25:39.175510Z"
     }
    },
    "outputs": [],
@@ -391,10 +391,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-13T23:40:54.634335Z",
-     "iopub.status.busy": "2023-02-13T23:40:54.633929Z",
-     "iopub.status.idle": "2023-02-13T23:40:56.105700Z",
-     "shell.execute_reply": "2023-02-13T23:40:56.105085Z"
+     "iopub.execute_input": "2023-02-24T23:25:39.179030Z",
+     "iopub.status.busy": "2023-02-24T23:25:39.178624Z",
+     "iopub.status.idle": "2023-02-24T23:25:40.584410Z",
+     "shell.execute_reply": "2023-02-24T23:25:40.583802Z"
     },
     "slideshow": {
      "slide_type": "slide"
@@ -418,10 +418,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-13T23:40:56.109177Z",
-     "iopub.status.busy": "2023-02-13T23:40:56.108624Z",
-     "iopub.status.idle": "2023-02-13T23:40:56.112651Z",
-     "shell.execute_reply": "2023-02-13T23:40:56.112076Z"
+     "iopub.execute_input": "2023-02-24T23:25:40.587815Z",
+     "iopub.status.busy": "2023-02-24T23:25:40.587283Z",
+     "iopub.status.idle": "2023-02-24T23:25:40.590826Z",
+     "shell.execute_reply": "2023-02-24T23:25:40.590158Z"
     },
     "slideshow": {
      "slide_type": "slide"
@@ -483,10 +483,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-13T23:40:56.115383Z",
-     "iopub.status.busy": "2023-02-13T23:40:56.114975Z",
-     "iopub.status.idle": "2023-02-13T23:41:55.045654Z",
-     "shell.execute_reply": "2023-02-13T23:41:55.044973Z"
+     "iopub.execute_input": "2023-02-24T23:25:40.593603Z",
+     "iopub.status.busy": "2023-02-24T23:25:40.593269Z",
+     "iopub.status.idle": "2023-02-24T23:26:38.349191Z",
+     "shell.execute_reply": "2023-02-24T23:26:38.348392Z"
     }
    },
    "outputs": [
@@ -494,119 +494,119 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2023-02-13 23:40:56,159]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
+      "\u001b[32m[I 2023-02-24 23:25:40,643]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2023-02-13 23:40:56,161]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
+      "\u001b[32m[I 2023-02-24 23:25:40,646]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:40:56] {806} INFO - trial 1 config: {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:25:40] {811} INFO - trial 1 config: {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:40:59] {215} INFO - result: {'expected_success': 0.6, 'success': 0.6, 'total_cost': 0.4624999999999999, 'cost': 0.4624999999999999, 'inference_cost': 0.023125, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}, 'config/model': 'code-davinci-002', 'config/temperature_or_top_p': {'temperature': 0.36865945026811975}, 'config/max_tokens': 347, 'config/n': 1, 'config/prompt': 1, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 3.7016141414642334}\n"
+      "[flaml.tune.tune: 02-24 23:25:44] {215} INFO - result: {'expected_success': 0.6, 'success': 0.6, 'total_cost': 0.4624999999999999, 'cost': 0.4624999999999999, 'inference_cost': 0.023125, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}, 'config/model': 'code-davinci-002', 'config/temperature_or_top_p': {'temperature': 0.36865945026811975}, 'config/max_tokens': 347, 'config/n': 1, 'config/prompt': 1, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 3.687161445617676}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:40:59] {806} INFO - trial 2 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:25:44] {811} INFO - trial 2 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:00] {215} INFO - result: {'expected_success': 0.35, 'success': 0.35, 'total_cost': 0.5671159999999997, 'cost': 0.104616, 'inference_cost': 0.0052308, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'temperature': 0.36865945026811975}, 'config/max_tokens': 347, 'config/n': 1, 'config/prompt': 1, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.673302412033081}\n"
+      "[flaml.tune.tune: 02-24 23:25:45] {215} INFO - result: {'expected_success': 0.35, 'success': 0.35, 'total_cost': 0.5671159999999997, 'cost': 0.104616, 'inference_cost': 0.0052308, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.36865945026811975}, 'max_tokens': 347, 'n': 1, 'prompt': 1, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'temperature': 0.36865945026811975}, 'config/max_tokens': 347, 'config/n': 1, 'config/prompt': 1, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.6666913032531738}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:00] {806} INFO - trial 3 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.4985070123025904}, 'max_tokens': 97, 'n': 20, 'prompt': 0, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:25:45] {811} INFO - trial 3 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.4985070123025904}, 'max_tokens': 97, 'n': 20, 'prompt': 0, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:17] {215} INFO - result: {'expected_success': 0.5080706992649381, 'success': 0.55, 'total_cost': 1.1848999999999996, 'cost': 0.617784, 'inference_cost': 0.0287676, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.4985070123025904}, 'max_tokens': 97, 'n': 20, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.4985070123025904}, 'config/max_tokens': 97, 'config/n': 20, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 16.56331181526184}\n"
+      "[flaml.tune.tune: 02-24 23:26:01] {215} INFO - result: {'expected_success': 0.5080706992649381, 'success': 0.55, 'total_cost': 1.1424679999999998, 'cost': 0.575352, 'inference_cost': 0.0287676, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.4985070123025904}, 'max_tokens': 97, 'n': 20, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.4985070123025904}, 'config/max_tokens': 97, 'config/n': 20, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 16.66586470603943}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:17] {806} INFO - trial 4 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:26:01] {811} INFO - trial 4 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:51] {215} INFO - result: {'expected_success': 0.6186627404336135, 'success': 0.65, 'total_cost': 2.4239719999999987, 'cost': 1.2390720000000002, 'inference_cost': 0.059620799999999995, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.6125260668293881}, 'config/max_tokens': 433, 'config/n': 29, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 34.57707595825195}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {215} INFO - result: {'expected_success': 0.6186627404336135, 'success': 0.65, 'total_cost': 2.3693479999999987, 'cost': 1.2268800000000002, 'inference_cost': 0.059620799999999995, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.6125260668293881}, 'config/max_tokens': 433, 'config/n': 29, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 36.605130434036255}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:51] {806} INFO - trial 5 config: {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.6177669784693172}, 'max_tokens': 231, 'n': 65, 'prompt': 3, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {811} INFO - trial 5 config: {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.6177669784693172}, 'max_tokens': 231, 'n': 65, 'prompt': 3, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:51] {215} INFO - result: {'expected_success': 0, 'total_cost': 2.6356719999999987, 'cost': 0.2117, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.6177669784693172}, 'max_tokens': 231, 'n': 65, 'prompt': 3, 'stop': 0}, 'config/model': 'code-davinci-002', 'config/temperature_or_top_p': {'temperature': 0.6177669784693172}, 'config/max_tokens': 231, 'config/n': 65, 'config/prompt': 3, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.0022132396697998047}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {215} INFO - result: {'expected_success': 0, 'total_cost': 2.5295479999999984, 'cost': 0.1602, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'temperature_or_top_p': {'temperature': 0.6177669784693172}, 'max_tokens': 231, 'n': 65, 'prompt': 3, 'stop': 0}, 'config/model': 'code-davinci-002', 'config/temperature_or_top_p': {'temperature': 0.6177669784693172}, 'config/max_tokens': 231, 'config/n': 65, 'config/prompt': 3, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.0020499229431152344}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:51] {806} INFO - trial 6 config: {'model': 'code-davinci-002', 'max_tokens': 263, 'n': 41, 'prompt': 0, 'stop': 0, 'temperature_or_top_p': {'top_p': 0.49834557213253655}}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {811} INFO - trial 6 config: {'model': 'code-davinci-002', 'max_tokens': 263, 'n': 41, 'prompt': 0, 'stop': 0, 'temperature_or_top_p': {'top_p': 0.49834557213253655}}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:54] {215} INFO - result: {'expected_success': 0, 'total_cost': 3.003171999999999, 'cost': 0.3675, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'max_tokens': 263, 'n': 41, 'prompt': 0, 'stop': 0, 'temperature_or_top_p': {'top_p': 0.49834557213253655}}, 'config/model': 'code-davinci-002', 'config/max_tokens': 263, 'config/n': 41, 'config/prompt': 0, 'config/stop': 0, 'config/temperature_or_top_p': {'top_p': 0.49834557213253655}, 'experiment_tag': 'exp', 'time_total_s': 3.3002660274505615}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {215} INFO - result: {'expected_success': 0, 'total_cost': 2.8578479999999984, 'cost': 0.32830000000000004, 'training_iteration': 0, 'config': {'model': 'code-davinci-002', 'max_tokens': 263, 'n': 41, 'prompt': 0, 'stop': 0, 'temperature_or_top_p': {'top_p': 0.49834557213253655}}, 'config/model': 'code-davinci-002', 'config/max_tokens': 263, 'config/n': 41, 'config/prompt': 0, 'config/stop': 0, 'config/temperature_or_top_p': {'top_p': 0.49834557213253655}, 'experiment_tag': 'exp', 'time_total_s': 0.002808809280395508}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:55] {806} INFO - trial 7 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.8286813263076767}, 'max_tokens': 57, 'n': 63, 'prompt': 3, 'stop': 0}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {811} INFO - trial 7 config: {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.8286813263076767}, 'max_tokens': 57, 'n': 63, 'prompt': 3, 'stop': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:55] {215} INFO - result: {'expected_success': 0, 'total_cost': 4.046379999999999, 'cost': 1.043208, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.8286813263076767}, 'max_tokens': 57, 'n': 63, 'prompt': 3, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'temperature': 0.8286813263076767}, 'config/max_tokens': 57, 'config/n': 63, 'config/prompt': 3, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.007852792739868164}\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {215} INFO - result: {'expected_success': 0, 'total_cost': 4.028831999999999, 'cost': 1.170984, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'temperature': 0.8286813263076767}, 'max_tokens': 57, 'n': 63, 'prompt': 3, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'temperature': 0.8286813263076767}, 'config/max_tokens': 57, 'config/n': 63, 'config/prompt': 3, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 0.015198230743408203}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[flaml.tune.tune: 02-13 23:41:55] {827} WARNING - fail to sample a trial for 100 times in a row, stopping.\n"
+      "[flaml.tune.tune: 02-24 23:26:38] {834} WARNING - fail to sample a trial for 100 times in a row, stopping.\n"
      ]
     }
    ],
@@ -656,10 +656,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-13T23:41:55.049204Z",
-     "iopub.status.busy": "2023-02-13T23:41:55.048871Z",
-     "iopub.status.idle": "2023-02-13T23:41:55.053284Z",
-     "shell.execute_reply": "2023-02-13T23:41:55.052574Z"
+     "iopub.execute_input": "2023-02-24T23:26:38.352710Z",
+     "iopub.status.busy": "2023-02-24T23:26:38.352378Z",
+     "iopub.status.idle": "2023-02-24T23:26:38.356939Z",
+     "shell.execute_reply": "2023-02-24T23:26:38.356217Z"
     }
    },
    "outputs": [
@@ -668,7 +668,7 @@
      "output_type": "stream",
      "text": [
       "optimized config {'model': 'code-cushman-001', 'max_tokens': 433, 'n': 29, 'prompt': '{prompt}', 'stop': ['\\nclass', '\\ndef', '\\nif', '\\nprint'], 'top_p': 0.6125260668293881}\n",
-      "best result on tuning data {'expected_success': 0.6186627404336135, 'success': 0.65, 'total_cost': 2.4239719999999987, 'cost': 1.2390720000000002, 'inference_cost': 0.059620799999999995, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.6125260668293881}, 'config/max_tokens': 433, 'config/n': 29, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 34.57707595825195}\n"
+      "best result on tuning data {'expected_success': 0.6186627404336135, 'success': 0.65, 'total_cost': 2.3693479999999987, 'cost': 1.2268800000000002, 'inference_cost': 0.059620799999999995, 'training_iteration': 0, 'config': {'model': 'code-cushman-001', 'temperature_or_top_p': {'top_p': 0.6125260668293881}, 'max_tokens': 433, 'n': 29, 'prompt': 0, 'stop': 0}, 'config/model': 'code-cushman-001', 'config/temperature_or_top_p': {'top_p': 0.6125260668293881}, 'config/max_tokens': 433, 'config/n': 29, 'config/prompt': 0, 'config/stop': 0, 'experiment_tag': 'exp', 'time_total_s': 36.605130434036255}\n"
      ]
     }
    ],
@@ -696,10 +696,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-13T23:41:55.056205Z",
-     "iopub.status.busy": "2023-02-13T23:41:55.055631Z",
-     "iopub.status.idle": "2023-02-13T23:41:56.039259Z",
-     "shell.execute_reply": "2023-02-13T23:41:56.038427Z"
+     "iopub.execute_input": "2023-02-24T23:26:38.359902Z",
+     "iopub.status.busy": "2023-02-24T23:26:38.359506Z",
+     "iopub.status.idle": "2023-02-24T23:26:39.343921Z",
+     "shell.execute_reply": "2023-02-24T23:26:39.343051Z"
     },
     "slideshow": {
      "slide_type": "subslide"
@@ -921,7 +921,7 @@
    "source": [
     "### Evaluate the success rate on the test data\n",
     "\n",
-    "You can use flaml's `oai.Completion.eval` to evaluate the performance of an entire dataset with the tuned config. To do that you need to set `oai.Completion.data` to the data to evaluate. The following code will take a while to evaluate all the 144 test data instances. Compared to the baseline success rate (0.46) on the [HELM benchmark](https://crfm.stanford.edu/helm/latest/?group=code_humaneval), the tuned config has a success rate of 0.68. It can be further improved if the inference budget and optimization budget are further increased."
+    "You can use flaml's `oai.Completion.eval` to evaluate the performance of an entire dataset with the tuned config. To do that you need to set `oai.Completion.data` to the data to evaluate. The following code will take a while to evaluate all the 144 test data instances. Compared to the baseline success rate (46%) on the [HELM benchmark](https://crfm.stanford.edu/helm/latest/?group=code_humaneval), the tuned config has a success rate of 68%. It can be further improved if the inference budget and optimization budget are further increased."
    ]
   },
   {
@@ -929,10 +929,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2023-02-13T23:53:05.597643Z",
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+     "iopub.execute_input": "2023-02-24T23:26:39.347295Z",
+     "iopub.status.busy": "2023-02-24T23:26:39.346994Z",
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@@ -940,7 +940,7 @@
      "name": "stdout",
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diff --git a/setup.py b/setup.py
index 86fea18b9..56bb955a8 100644
--- a/setup.py
+++ b/setup.py
@@ -92,7 +92,14 @@ setuptools.setup(
         "vw": [
             "vowpalwabbit>=8.10.0, <9.0.0",
         ],
-        "nlp": [
+        "hf": [
+            "transformers[torch]==4.26",
+            "datasets",
+            "nltk",
+            "rouge_score",
+            "seqeval",
+        ],
+        "nlp": [  # for backward compatibility; hf is the new option name
             "transformers[torch]==4.26",
             "datasets",
             "nltk",
diff --git a/website/docs/Examples/AutoML-NLP.md b/website/docs/Examples/AutoML-NLP.md
index 7af14d908..409ac8872 100644
--- a/website/docs/Examples/AutoML-NLP.md
+++ b/website/docs/Examples/AutoML-NLP.md
@@ -2,9 +2,9 @@
 
 ### Requirements
 
-This example requires GPU. Install the [nlp] option:
+This example requires GPU. Install the [hf] option:
 ```python
-pip install "flaml[nlp]"
+pip install "flaml[hf]"
 ```
 
 ### A simple sequence classification example
diff --git a/website/docs/Getting-Started.md b/website/docs/Getting-Started.md
index e3e828cbc..c2d498ae9 100644
--- a/website/docs/Getting-Started.md
+++ b/website/docs/Getting-Started.md
@@ -3,17 +3,17 @@
 <!-- ### Welcome to FLAML, a Fast Library for Automated Machine Learning & Tuning! -->
 
 FLAML is a lightweight Python library that finds accurate machine
-learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.
+learning models automatically, efficiently and economically. It frees users from selecting models and hyperparameters for each model.
 
 ### Main Features
 
-1. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks.
+1. For common machine learning or AI tasks like classification, regression, and generation, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks, including large language models such as the OpenAI GPT-3 models.
 
 2. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code). Users can customize only when and what they need to, and leave the rest to the library.
 
 3. It supports fast and economical automatic tuning, capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
 hyperparameter optimization](Use-Cases/Tune-User-Defined-Function#hyperparameter-optimization-algorithm)
-and learner selection method invented by Microsoft Research.
+and model selection method invented by Microsoft Research, and many followup [research studies](Research).
 
 ### Quickstart
 
diff --git a/website/docs/Installation.md b/website/docs/Installation.md
index 8a4d4dc27..92285a703 100644
--- a/website/docs/Installation.md
+++ b/website/docs/Installation.md
@@ -24,8 +24,11 @@ install flaml with the [notebook] option:
 pip install flaml[notebook]
 ```
 
-#### Extra learners
-
+#### Extra learners/models
+* openai models
+```bash
+pip install flaml[openai]
+```
 * catboost
 ```bash
 pip install flaml[catboost]
@@ -38,10 +41,9 @@ pip install flaml[vw]
 ```bash
 pip install flaml[forecast]
 ```
-
-* natural language processing: transformers
+* huggingface transformers
 ```bash
-pip install flaml[nlp]
+pip install flaml[hf]
 ```
 
 #### Distributed tuning