first commit

README.md

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+---
+language: 
+- en
+thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
+tags:
+- text-classification
+- emotion
+- pytorch
+license: apache-2.0
+datasets:
+- emotion
+metrics:
+- Accuracy, F1 Score
+---
+# Distilbert-base-uncased-emotion
+
+## Model description:
+[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
+
+[Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
+```
+ learning rate 2e-5, 
+ batch size 64,
+ num_train_epochs=8,
+```
+
+## Model Performance Comparision on Emotion Dataset from Twitter:
+
+| Model | Accuracy | F1 Score |  Test Sample per Second |
+| --- | --- | --- | --- |
+| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |
+| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |
+| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |
+| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |
+
+## How to Use the model:
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
+prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
+print(prediction)
+
+"""
+Output:
+[[
+{'label': 'sadness', 'score': 0.0006792712374590337}, 
+{'label': 'joy', 'score': 0.9959300756454468}, 
+{'label': 'love', 'score': 0.0009452480007894337}, 
+{'label': 'anger', 'score': 0.0018055217806249857}, 
+{'label': 'fear', 'score': 0.00041110432357527316}, 
+{'label': 'surprise', 'score': 0.0002288572577526793}
+]]
+"""
+```
+
+## Dataset:
+[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
+
+## Training procedure
+[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
+
+## Eval results
+```json
+{
+'test_accuracy': 0.938,
+ 'test_f1': 0.937932884041714,
+ 'test_loss': 0.1472451239824295,
+ 'test_mem_cpu_alloc_delta': 0,
+ 'test_mem_cpu_peaked_delta': 0,
+ 'test_mem_gpu_alloc_delta': 0,
+ 'test_mem_gpu_peaked_delta': 163454464,
+ 'test_runtime': 5.0164,
+ 'test_samples_per_second': 398.69
+ }
+```
+
+## Reference:
+* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)

config.json

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+{
+  "_name_or_path": "./",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "sadness",
+    "1": "joy",
+    "2": "love",
+    "3": "anger",
+    "4": "fear",
+    "5": "surprise"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "anger": 3,
+    "fear": 4,
+    "joy": 1,
+    "love": 2,
+    "sadness": 0,
+    "surprise": 5
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.11.0.dev0",
+  "vocab_size": 30522
+}

convert_flax_to_pytorch.py

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+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("./", from_flax=True)
+model.save_pretrained("./")

convert_pytorch_to_flax.py

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+from transformers import FlaxAutoModelForSequenceClassification
+model = FlaxAutoModelForSequenceClassification.from_pretrained("./", from_pt=True)
+model.save_pretrained("./")

convert_pytorch_to_tensorflow.py

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+from transformers import TFAutoModelForSequenceClassification
+model = TFAutoModelForSequenceClassification.from_pretrained("./", from_pt=True)
+model.save_pretrained("./")

flax_model.msgpack

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+size 267836005

pytorch_model.bin

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tf_model.h5

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tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased"}