Emotion-Analysis-with-BERT.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30559,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/tirendazacademy/emotion-analysis-with-bert?scriptVersionId=155420754\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'> Emotion Analysis with TensorFlow, Hugging Face and BERT </div></b>\n\n![](https://img.freepik.com/free-vector/girl-face-avatar-with-different-emotion-expression_107791-19830.jpg?w=1380&t=st=1697191626~exp=1697192226~hmac=cd3a951df743ba2f0fc074031cad0402884b67e8bc77f68947b375c0021208d5)","metadata":{}},{"cell_type":"markdown","source":"Hi guys 😀 In this notebook, we're going to cover how to perform sentiment analysis with Hugging Face and TensorFlow. Let's get started.","metadata":{}},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>1. Dataset Loading </div></b>","metadata":{}},{"cell_type":"code","source":"!pip install -q -U datasets","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-17T20:31:07.647289Z","iopub.execute_input":"2023-12-17T20:31:07.648086Z","iopub.status.idle":"2023-12-17T20:31:24.516272Z","shell.execute_reply.started":"2023-12-17T20:31:07.648044Z","shell.execute_reply":"2023-12-17T20:31:24.515068Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"from datasets import load_dataset\nemotions = load_dataset(\"dair-ai/emotion\")\nemotions","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:24.518921Z","iopub.execute_input":"2023-12-17T20:31:24.519346Z","iopub.status.idle":"2023-12-17T20:31:29.91473Z","shell.execute_reply.started":"2023-12-17T20:31:24.519307Z","shell.execute_reply":"2023-12-17T20:31:29.913806Z"},"trusted":true},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading builder script:   0%|          | 0.00/3.97k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eabb39a9ea57437f8b57c2bb917c7c0d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading metadata:   0%|          | 0.00/3.28k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9524fbe671849be8a2877347d7262d4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading readme:   0%|          | 0.00/8.78k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bade128624d84a2c8ced74ce78ab1a7c"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0fff015cb75b4545a1014ad1ff804144"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/592k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"42e0f2f505d34399bf4919a1bf2d9206"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/74.0k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"109ed2f647cc4d6ab43ead4628dc2e91"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/74.9k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9e0f955943e24871a0eac57c26d93af8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"12e2583eebe944ffbfe84557178fdec5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0931e682516d4979a78bd3f1600b6326"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c46203f5e7fe455282e6ee33aa5f5c0d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"efd41ea7e4cf4ac88b4ee8f1a5a198e6"}},"metadata":{}},{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"DatasetDict({\n    train: Dataset({\n        features: ['text', 'label'],\n        num_rows: 16000\n    })\n    validation: Dataset({\n        features: ['text', 'label'],\n        num_rows: 2000\n    })\n    test: Dataset({\n        features: ['text', 'label'],\n        num_rows: 2000\n    })\n})"},"metadata":{}}]},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>2. Understanding the Dataset </div></b>","metadata":{}},{"cell_type":"code","source":"emotions[\"train\"][0]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:29.915935Z","iopub.execute_input":"2023-12-17T20:31:29.916278Z","iopub.status.idle":"2023-12-17T20:31:29.923116Z","shell.execute_reply.started":"2023-12-17T20:31:29.91625Z","shell.execute_reply":"2023-12-17T20:31:29.922158Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"{'text': 'i didnt feel humiliated', 'label': 0}"},"metadata":{}}]},{"cell_type":"code","source":"emotions[\"train\"].column_names","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:29.926218Z","iopub.execute_input":"2023-12-17T20:31:29.927108Z","iopub.status.idle":"2023-12-17T20:31:29.934222Z","shell.execute_reply.started":"2023-12-17T20:31:29.927073Z","shell.execute_reply":"2023-12-17T20:31:29.933327Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"['text', 'label']"},"metadata":{}}]},{"cell_type":"code","source":"emotions[\"train\"].features","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:29.935382Z","iopub.execute_input":"2023-12-17T20:31:29.935638Z","iopub.status.idle":"2023-12-17T20:31:29.948507Z","shell.execute_reply.started":"2023-12-17T20:31:29.935615Z","shell.execute_reply":"2023-12-17T20:31:29.94759Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"{'text': Value(dtype='string', id=None),\n 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}"},"metadata":{}}]},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>3. Data Preprocessing </div></b>","metadata":{}},{"cell_type":"code","source":"from transformers import AutoTokenizer\nmodel_ckpt = \"distilbert-base-uncased\"\ntokenizer = AutoTokenizer.from_pretrained(model_ckpt)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:29.949811Z","iopub.execute_input":"2023-12-17T20:31:29.950317Z","iopub.status.idle":"2023-12-17T20:31:34.247529Z","shell.execute_reply.started":"2023-12-17T20:31:29.950284Z","shell.execute_reply":"2023-12-17T20:31:34.246752Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5329d91587b24c9199187b3c5443f604"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ab07af9f86bb4e42a7265e6fc5ba71a8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"be18218f0f5f4016aa81eef0bb83331e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa41a2f740ab4741935f666e181e418f"}},"metadata":{}}]},{"cell_type":"code","source":"def tokenize(examples):\n    return tokenizer(examples[\"text\"], truncation=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:34.248548Z","iopub.execute_input":"2023-12-17T20:31:34.248949Z","iopub.status.idle":"2023-12-17T20:31:34.253382Z","shell.execute_reply.started":"2023-12-17T20:31:34.248925Z","shell.execute_reply":"2023-12-17T20:31:34.252537Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:34.254448Z","iopub.execute_input":"2023-12-17T20:31:34.25474Z","iopub.status.idle":"2023-12-17T20:31:35.672192Z","shell.execute_reply.started":"2023-12-17T20:31:34.254715Z","shell.execute_reply":"2023-12-17T20:31:35.671255Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/16000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"50c36cbf8f98417eb2a0377885cb83e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/2000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f3d0200954544a2ca7d8d218071f191e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/2000 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af6205964572424daa1a93fbf8f487ad"}},"metadata":{}}]},{"cell_type":"markdown","source":"## Padding","metadata":{}},{"cell_type":"code","source":"from transformers import DataCollatorWithPadding","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:35.673283Z","iopub.execute_input":"2023-12-17T20:31:35.673534Z","iopub.status.idle":"2023-12-17T20:31:54.558386Z","shell.execute_reply.started":"2023-12-17T20:31:35.673513Z","shell.execute_reply":"2023-12-17T20:31:54.557588Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"data_collator=DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:54.561594Z","iopub.execute_input":"2023-12-17T20:31:54.562169Z","iopub.status.idle":"2023-12-17T20:31:54.566894Z","shell.execute_reply.started":"2023-12-17T20:31:54.56212Z","shell.execute_reply":"2023-12-17T20:31:54.565856Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"## Evaluate","metadata":{}},{"cell_type":"code","source":"!pip install -q evaluate","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:31:54.568008Z","iopub.execute_input":"2023-12-17T20:31:54.568323Z","iopub.status.idle":"2023-12-17T20:32:07.367147Z","shell.execute_reply.started":"2023-12-17T20:31:54.568298Z","shell.execute_reply":"2023-12-17T20:32:07.365894Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n","output_type":"stream"}]},{"cell_type":"code","source":"import evaluate\naccuracy = evaluate.load(\"accuracy\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:32:07.369113Z","iopub.execute_input":"2023-12-17T20:32:07.36955Z","iopub.status.idle":"2023-12-17T20:32:11.64043Z","shell.execute_reply.started":"2023-12-17T20:32:07.369513Z","shell.execute_reply":"2023-12-17T20:32:11.639607Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ecd41ee05e7c40caa80b9edd371416f2"}},"metadata":{}}]},{"cell_type":"code","source":"import numpy as np \n\ndef compute_metrics(eval_pred):\n    predictions, labels = eval_pred\n    predictions = np.argmax(predictions, axis=1)\n    return accuracy.compute(predictions=predictions, \n                            references = labels)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:32:11.641555Z","iopub.execute_input":"2023-12-17T20:32:11.641812Z","iopub.status.idle":"2023-12-17T20:32:11.646709Z","shell.execute_reply.started":"2023-12-17T20:32:11.64179Z","shell.execute_reply":"2023-12-17T20:32:11.645753Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>4. Model Training </div></b>","metadata":{}},{"cell_type":"code","source":"from transformers import TFAutoModelForSequenceClassification\n\nid2label={0:\"sadness\",1:\"joy\",2:\"love\",3:\"anger\",4:\"fear\",5:\"suprise\"}\nlabel2id={\"sadness\":0,\"joy\":1,\"love\":2,\"anger\":3,\"fear\":4,\"suprise\":5}\n\nmodel=TFAutoModelForSequenceClassification.from_pretrained(\n    model_ckpt, num_labels=6, id2label=id2label, label2id=label2id\n)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:32:11.64804Z","iopub.execute_input":"2023-12-17T20:32:11.649202Z","iopub.status.idle":"2023-12-17T20:32:29.438769Z","shell.execute_reply.started":"2023-12-17T20:32:11.649162Z","shell.execute_reply":"2023-12-17T20:32:29.438026Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d54d12843dff472c88c2e20fd0e0f4bd"}},"metadata":{}},{"name":"stderr","text":"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight']\n- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\nSome weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"}]},{"cell_type":"code","source":"from huggingface_hub import notebook_login\nnotebook_login()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:35:20.143541Z","iopub.execute_input":"2023-12-17T20:35:20.143975Z","iopub.status.idle":"2023-12-17T20:35:21.914221Z","shell.execute_reply.started":"2023-12-17T20:35:20.143945Z","shell.execute_reply":"2023-12-17T20:35:21.913165Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nToken will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\nToken is valid (permission: write).\nYour token has been saved to /root/.cache/huggingface/token\nLogin successful\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Preparing the datasets","metadata":{}},{"cell_type":"code","source":"tf_train_set = model.prepare_tf_dataset(\n    emotions_encoded[\"train\"],\n    shuffle = True,\n    batch_size = 16,\n    collate_fn = data_collator\n)\n\ntf_validation_set = model.prepare_tf_dataset(\n    emotions_encoded[\"validation\"],\n    shuffle = True,\n    batch_size = 16,\n    collate_fn = data_collator\n)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:35:30.272666Z","iopub.execute_input":"2023-12-17T20:35:30.273069Z","iopub.status.idle":"2023-12-17T20:35:30.926659Z","shell.execute_reply.started":"2023-12-17T20:35:30.273038Z","shell.execute_reply":"2023-12-17T20:35:30.925324Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stderr","text":"You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n","output_type":"stream"}]},{"cell_type":"code","source":"import tensorflow as tf\n\noptimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\nmodel.compile(optimizer=optimizer)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:35:30.929362Z","iopub.execute_input":"2023-12-17T20:35:30.930091Z","iopub.status.idle":"2023-12-17T20:35:30.957605Z","shell.execute_reply.started":"2023-12-17T20:35:30.930048Z","shell.execute_reply":"2023-12-17T20:35:30.956549Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"## Setting the callbacks","metadata":{}},{"cell_type":"code","source":"from transformers.keras_callbacks import KerasMetricCallback\nfrom transformers.keras_callbacks import PushToHubCallback\n\nmetric_callback=KerasMetricCallback(\n    metric_fn=compute_metrics,\n    eval_dataset = tf_validation_set\n)\n\npush_to_hub_callback=PushToHubCallback(\n    output_dir = \"emotion-analysis-with-distilbert\",\n    tokenizer = tokenizer\n)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:35:30.958866Z","iopub.execute_input":"2023-12-17T20:35:30.959322Z","iopub.status.idle":"2023-12-17T20:37:08.825104Z","shell.execute_reply.started":"2023-12-17T20:35:30.959283Z","shell.execute_reply":"2023-12-17T20:37:08.824248Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\nFor more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n  warnings.warn(warning_message, FutureWarning)\nCloning https://huggingface.co/Tirendaz/emotion-analysis-with-distilbert into local empty directory.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Download file tf_model.h5:   0%|          | 7.40k/256M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"573091ce5f124b3d8a526f7fde81f5f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Clean file tf_model.h5:   0%|          | 1.00k/256M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cf2ebeaa7ddd4f6a961a1808bb05c3ac"}},"metadata":{}}]},{"cell_type":"code","source":"model.fit(x=tf_train_set,\n         validation_data=tf_validation_set,\n         epochs=2,\n         callbacks=[metric_callback, push_to_hub_callback])","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:37:08.827415Z","iopub.execute_input":"2023-12-17T20:37:08.827724Z","iopub.status.idle":"2023-12-17T20:42:00.752569Z","shell.execute_reply.started":"2023-12-17T20:37:08.827698Z","shell.execute_reply":"2023-12-17T20:42:00.751552Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Epoch 1/2\n1000/1000 [==============================] - 173s 143ms/step - loss: 0.3781 - val_loss: 0.1645 - accuracy: 0.9270\nEpoch 2/2\n1000/1000 [==============================] - 111s 111ms/step - loss: 0.1347 - val_loss: 0.1393 - accuracy: 0.9370\n","output_type":"stream"},{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7c61f476ca90>"},"metadata":{}}]},{"cell_type":"markdown","source":"# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>5. Prediction </div></b>","metadata":{}},{"cell_type":"code","source":"custom_text = \"I watched a movie yesterday. It was really awesome\"","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:42:00.753794Z","iopub.execute_input":"2023-12-17T20:42:00.754133Z","iopub.status.idle":"2023-12-17T20:42:00.758591Z","shell.execute_reply.started":"2023-12-17T20:42:00.754106Z","shell.execute_reply":"2023-12-17T20:42:00.757519Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"from transformers import pipeline\n\nclassifier = pipeline(\"sentiment-analysis\", \n                     model = \"Tirendaz/emotion-analysis-with-distilbert\")","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-12-17T20:42:00.759836Z","iopub.execute_input":"2023-12-17T20:42:00.760167Z","iopub.status.idle":"2023-12-17T20:42:06.150734Z","shell.execute_reply.started":"2023-12-17T20:42:00.760117Z","shell.execute_reply":"2023-12-17T20:42:06.149879Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/782 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"598aa516e6cc486ebff031ed099414bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tf_model.h5:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d7325fc299f742ecb90043394f1854c9"}},"metadata":{}},{"name":"stderr","text":"Some layers from the model checkpoint at Tirendaz/emotion-analysis-with-distilbert were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\nSome layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at Tirendaz/emotion-analysis-with-distilbert and are newly initialized: ['dropout_39']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/320 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4961f5ce481438c92ab0b2c8f94a477"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0196414508bd4783b4d7c66150d59641"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dbd8095aaa49452b92f3664208274507"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c10d8f0b3d64463ea87a80fe69610cea"}},"metadata":{}}]},{"cell_type":"code","source":"inputs=tokenizer(custom_text, return_tensors= \"tf\")\nlogits = model(**inputs).logits\npredicted_class_id=int(tf.math.argmax(logits, axis=-1)[0])\nmodel.config.id2label[predicted_class_id]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T20:42:06.15178Z","iopub.execute_input":"2023-12-17T20:42:06.152069Z","iopub.status.idle":"2023-12-17T20:42:06.27546Z","shell.execute_reply.started":"2023-12-17T20:42:06.152043Z","shell.execute_reply":"2023-12-17T20:42:06.274556Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"'joy'"},"metadata":{}}]},{"cell_type":"markdown","source":"**Thanks for reading! If you enjoyed this notebook, don't forget to upvote** 👍\n\n*Let's connect* [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [X](http://x.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) 😎\n\n## Resource\n\n- [NLP with Transformers](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb)","metadata":{}}]}