Emotion-Analysis-with-BERT-and-TensorFlow.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"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/tirendazacademy/emotion-analysis-with-bert-and-tensorflow?scriptVersionId=151313973\" 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":"# Load Emotion Dataset","metadata":{}},{"cell_type":"code","source":"!pip install -q datasets","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-18T22:30:41.68897Z","iopub.execute_input":"2023-11-18T22:30:41.689277Z","iopub.status.idle":"2023-11-18T22:30:56.107375Z","shell.execute_reply.started":"2023-11-18T22:30:41.689249Z","shell.execute_reply":"2023-11-18T22:30:56.106052Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"from datasets import load_dataset\nemotions = load_dataset(\"dair-ai/emotion\")","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:30:56.109579Z","iopub.execute_input":"2023-11-18T22:30:56.109955Z","iopub.status.idle":"2023-11-18T22:31:01.162723Z","shell.execute_reply.started":"2023-11-18T22:30:56.109926Z","shell.execute_reply":"2023-11-18T22:31:01.1619Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"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":"a571656f9d9b4d39bd343bd327603f22"}},"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":"9adb587bec9746bfb5c885ff6a67bd46"}},"metadata":{}},{"name":"stdout","text":"Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\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":"3ff9c127d7ba46f99ae631d308f179ce"}},"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":"cc10292d2c5048deaf1a9646703aff85"}},"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":"3336cd78e77b453682e6e14e47de76a8"}},"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":"8fe8a216c1ce4b2f86cde5a2a3237565"}},"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":"883f976b6dd54f548b1b980216cc9225"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating train split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating validation split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating test split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"name":"stdout","text":"Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a182ae71dc8340939f8ce2d221f7aff5"}},"metadata":{}}]},{"cell_type":"code","source":"emotions","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:01.163818Z","iopub.execute_input":"2023-11-18T22:31:01.164295Z","iopub.status.idle":"2023-11-18T22:31:01.170983Z","shell.execute_reply.started":"2023-11-18T22:31:01.164268Z","shell.execute_reply":"2023-11-18T22:31:01.1699Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"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":"code","source":"emotions[\"train\"][0]","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:01.173097Z","iopub.execute_input":"2023-11-18T22:31:01.173375Z","iopub.status.idle":"2023-11-18T22:31:01.181796Z","shell.execute_reply.started":"2023-11-18T22:31:01.173342Z","shell.execute_reply":"2023-11-18T22:31:01.180916Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"{'text': 'i didnt feel humiliated', 'label': 0}"},"metadata":{}}]},{"cell_type":"markdown","source":"# Preprocess","metadata":{}},{"cell_type":"code","source":"from transformers import AutoTokenizer\n\nmodel_ckpt = \"distilbert-base-uncased\"\ntokenizer = AutoTokenizer.from_pretrained(model_ckpt)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:01.182895Z","iopub.execute_input":"2023-11-18T22:31:01.183203Z","iopub.status.idle":"2023-11-18T22:31:05.378417Z","shell.execute_reply.started":"2023-11-18T22:31:01.183179Z","shell.execute_reply":"2023-11-18T22:31:05.377435Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading 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":"a3893fb0e5594072b8d672699f8dae02"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"94f02e8aa97d484699289a19f192b619"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2418fad9b5f64ebfabe06619cc091fbe"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"41d1a93debc1473a8c50e37eb9f9d59c"}},"metadata":{}}]},{"cell_type":"code","source":"def tokenize(examples):\n    return tokenizer(examples[\"text\"], truncation=True)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:05.380532Z","iopub.execute_input":"2023-11-18T22:31:05.381064Z","iopub.status.idle":"2023-11-18T22:31:05.385616Z","shell.execute_reply.started":"2023-11-18T22:31:05.381035Z","shell.execute_reply":"2023-11-18T22:31:05.384648Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"emotions_encoded = emotions.map(tokenize, batched=True,batch_size=None)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:05.386691Z","iopub.execute_input":"2023-11-18T22:31:05.386984Z","iopub.status.idle":"2023-11-18T22:31:06.721969Z","shell.execute_reply.started":"2023-11-18T22:31:05.38696Z","shell.execute_reply":"2023-11-18T22:31:06.720896Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/1 [00:00<?, ?ba/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8da76aa2c04d48dd8dda5b8d0e49e7ba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/1 [00:00<?, ?ba/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e917aabfd1a4360adcc8fd6367127b2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/1 [00:00<?, ?ba/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8a2a0b360094a77bed55b93e161ec7d"}},"metadata":{}}]},{"cell_type":"code","source":"from transformers import DataCollatorWithPadding\n\ndata_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:06.723051Z","iopub.execute_input":"2023-11-18T22:31:06.723292Z","iopub.status.idle":"2023-11-18T22:31:25.65218Z","shell.execute_reply.started":"2023-11-18T22:31:06.72327Z","shell.execute_reply":"2023-11-18T22:31:25.651268Z"},"trusted":true},"execution_count":8,"outputs":[{"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"}]},{"cell_type":"markdown","source":"# Evaluate","metadata":{}},{"cell_type":"code","source":"!pip install -q evaluate","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:25.653538Z","iopub.execute_input":"2023-11-18T22:31:25.654363Z","iopub.status.idle":"2023-11-18T22:31:37.974758Z","shell.execute_reply.started":"2023-11-18T22:31:25.654325Z","shell.execute_reply":"2023-11-18T22:31:37.973492Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":9,"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\n\naccuracy = evaluate.load(\"accuracy\")","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:37.978095Z","iopub.execute_input":"2023-11-18T22:31:37.97841Z","iopub.status.idle":"2023-11-18T22:31:42.333842Z","shell.execute_reply.started":"2023-11-18T22:31:37.978383Z","shell.execute_reply":"2023-11-18T22:31:42.333134Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":10,"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":"18b9a75804134d78884559e46cfc6c11"}},"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, references=labels)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:42.335145Z","iopub.execute_input":"2023-11-18T22:31:42.335424Z","iopub.status.idle":"2023-11-18T22:31:42.340361Z","shell.execute_reply.started":"2023-11-18T22:31:42.335399Z","shell.execute_reply":"2023-11-18T22:31:42.339388Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"# Train","metadata":{}},{"cell_type":"code","source":"id2label = {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}","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:42.341938Z","iopub.execute_input":"2023-11-18T22:31:42.342272Z","iopub.status.idle":"2023-11-18T22:31:42.364361Z","shell.execute_reply.started":"2023-11-18T22:31:42.342241Z","shell.execute_reply":"2023-11-18T22:31:42.363435Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"from transformers import TFAutoModelForSequenceClassification\n\nmodel = TFAutoModelForSequenceClassification.from_pretrained(\n    \"distilbert-base-uncased\", num_labels=6, id2label=id2label, label2id=label2id\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:42.365325Z","iopub.execute_input":"2023-11-18T22:31:42.36559Z","iopub.status.idle":"2023-11-18T22:31:59.971825Z","shell.execute_reply.started":"2023-11-18T22:31:42.365566Z","shell.execute_reply":"2023-11-18T22:31:59.971107Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"942d5f40d37a4158b46506c3c50a6579"}},"metadata":{}},{"name":"stderr","text":"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias']\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":"markdown","source":"## Login to HuggingFace","metadata":{}},{"cell_type":"code","source":"from huggingface_hub import notebook_login\n\nnotebook_login()","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:31:59.97289Z","iopub.execute_input":"2023-11-18T22:31:59.973158Z","iopub.status.idle":"2023-11-18T22:32:00.006759Z","shell.execute_reply.started":"2023-11-18T22:31:59.973133Z","shell.execute_reply":"2023-11-18T22:32:00.005819Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc7bf01fea084512b0bddd6d64781e55"}},"metadata":{}}]},{"cell_type":"markdown","source":"## Prepare the Dataset","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[\"test\"],\n    shuffle=False,\n    batch_size=16,\n    collate_fn=data_collator,\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:32:12.521033Z","iopub.execute_input":"2023-11-18T22:32:12.521934Z","iopub.status.idle":"2023-11-18T22:32:12.931689Z","shell.execute_reply.started":"2023-11-18T22:32:12.521899Z","shell.execute_reply":"2023-11-18T22:32:12.930845Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":15,"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":"markdown","source":"## Compile","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\n\noptimizer=tf.keras.optimizers.Adam(learning_rate=5e-5)\nmodel.compile(optimizer=optimizer)  # No loss argument!","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:32:12.933717Z","iopub.execute_input":"2023-11-18T22:32:12.934122Z","iopub.status.idle":"2023-11-18T22:32:13.227946Z","shell.execute_reply.started":"2023-11-18T22:32:12.934077Z","shell.execute_reply":"2023-11-18T22:32:13.226809Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"from transformers.keras_callbacks import KerasMetricCallback\n\nmetric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:32:13.229102Z","iopub.execute_input":"2023-11-18T22:32:13.229379Z","iopub.status.idle":"2023-11-18T22:32:13.237033Z","shell.execute_reply.started":"2023-11-18T22:32:13.229355Z","shell.execute_reply":"2023-11-18T22:32:13.236218Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"from transformers.keras_callbacks import PushToHubCallback\n\npush_to_hub_callback = PushToHubCallback(\n    output_dir=\"emotion-analysis-with-distilbert\",\n    tokenizer=tokenizer,\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:32:13.239753Z","iopub.execute_input":"2023-11-18T22:32:13.240146Z","iopub.status.idle":"2023-11-18T22:33:53.006977Z","shell.execute_reply.started":"2023-11-18T22:32:13.240121Z","shell.execute_reply":"2023-11-18T22:33:53.005862Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Cloning 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%|          | 8.00k/256M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3171bab94e8b47aa9f7a752cec3eccfe"}},"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":"591e51854b0542e08e892e4f93283061"}},"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":"2023-11-18T22:33:53.008295Z","iopub.execute_input":"2023-11-18T22:33:53.008677Z","iopub.status.idle":"2023-11-18T22:39:31.257917Z","shell.execute_reply.started":"2023-11-18T22:33:53.008648Z","shell.execute_reply":"2023-11-18T22:39:31.256838Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Epoch 1/2\n1000/1000 [==============================] - 176s 147ms/step - loss: 0.3754 - val_loss: 0.1718 - accuracy: 0.9255\nEpoch 2/2\n1000/1000 [==============================] - 112s 112ms/step - loss: 0.1310 - val_loss: 0.1362 - accuracy: 0.9320\n","output_type":"stream"},{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7deb603d23e0>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Inference","metadata":{}},{"cell_type":"code","source":"custom_text=\"I watched a movie yesterday. It was really bad.\"","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:39:31.259159Z","iopub.execute_input":"2023-11-18T22:39:31.259428Z","iopub.status.idle":"2023-11-18T22:39:31.264663Z","shell.execute_reply.started":"2023-11-18T22:39:31.259405Z","shell.execute_reply":"2023-11-18T22:39:31.26387Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"from transformers import pipeline\n\nclassifier = pipeline(\"sentiment-analysis\", \n                      model=\"Tirendaz/emotion-analysis-with-distilbert\")","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:39:31.265924Z","iopub.execute_input":"2023-11-18T22:39:31.266279Z","iopub.status.idle":"2023-11-18T22:39:39.267602Z","shell.execute_reply.started":"2023-11-18T22:39:31.266242Z","shell.execute_reply":"2023-11-18T22:39:39.266666Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading config.json:   0%|          | 0.00/782 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b08cb00269d04d7c94655dcc66f89143"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading 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":"1c2d6d71b3a3442080a31664d10f7f5a"}},"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":"Downloading 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":"d284d74843e44ea990177df4bfd1682d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b4bf3bba1573482881a647de607465be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"efe7bcaf31b2409c9c6ff3916fc2ba05"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading (…)cial_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":"806a4a674d03458f9b8267a31887a5de"}},"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":"2023-11-18T22:39:39.269039Z","iopub.execute_input":"2023-11-18T22:39:39.269352Z","iopub.status.idle":"2023-11-18T22:39:39.41571Z","shell.execute_reply.started":"2023-11-18T22:39:39.269319Z","shell.execute_reply":"2023-11-18T22:39:39.414868Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"'sadness'"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nlabels = emotions[\"train\"].features[\"label\"].names\npreds=classifier(custom_text, return_all_scores = True)\npreds_df = pd.DataFrame(preds[0])\nplt.bar(labels, 100*preds_df[\"score\"])\nplt.title(f'\"{custom_text}\"')\nplt.ylabel(\"Class probability (%)\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-11-18T22:39:39.416954Z","iopub.execute_input":"2023-11-18T22:39:39.417644Z","iopub.status.idle":"2023-11-18T22:39:40.152169Z","shell.execute_reply.started":"2023-11-18T22:39:39.417608Z","shell.execute_reply":"2023-11-18T22:39:40.151231Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n  warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"Thanks for reading. If you enjoyed this notebook, don't forget to upvote ☺️\n\nLet'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) 😎","metadata":{}}]}