Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- sentiment-analysis
+- text-classification
+- zero-shot-distillation
+- distillation
+- zero-shot-classification
+- debarta-v3
+model-index:
+- name: distilbert-base-multilingual-cased-sentiments-student
+  results: []
+datasets:
+- tyqiangz/multilingual-sentiments
+language:
+- en
+- ar
+- de
+- es
+- fr
+- ja
+- zh
+- id
+- hi
+- it
+- ms
+- pt
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# distilbert-base-multilingual-cased-sentiments-student
+
+This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment 
+dataset using this [script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation). 
+
+In reality the multilingual-sentiment dataset is annotated of course, 
+but we'll pretend and ignore the annotations for the sake of example.
+
+
+    Teacher model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
+    Teacher hypothesis template: "The sentiment of this text is {}."
+    Student model: distilbert-base-multilingual-cased
+
+
+## Inference example
+
+```python
+from transformers import pipeline
+
+distilled_student_sentiment_classifier = pipeline(
+    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", 
+    return_all_scores=True
+)
+
+# english
+distilled_student_sentiment_classifier ("I love this movie and i would watch it again and again!")
+>> [[{'label': 'positive', 'score': 0.9731044769287109},
+  {'label': 'neutral', 'score': 0.016910076141357422},
+  {'label': 'negative', 'score': 0.009985478594899178}]]
+
+# malay
+distilled_student_sentiment_classifier("Saya suka filem ini dan saya akan menontonnya lagi dan lagi!")
+[[{'label': 'positive', 'score': 0.9760093688964844},
+  {'label': 'neutral', 'score': 0.01804516464471817},
+  {'label': 'negative', 'score': 0.005945465061813593}]]
+
+# japanese
+distilled_student_sentiment_classifier("私はこの映画が大好きで、何度も見ます！")
+>> [[{'label': 'positive', 'score': 0.9342429041862488},
+  {'label': 'neutral', 'score': 0.040193185210227966},
+  {'label': 'negative', 'score': 0.025563929229974747}]]
+
+
+```
+
+
+## Training procedure
+
+Notebook link: TBU
+
+### Training hyperparameters
+
+Result can be reproduce using the following commands:
+
+```bash
+python transformers/examples/research_projects/zero-shot-distillation/distill_classifier.py \
+--data_file ./multilingual-sentiments/train_unlabeled.txt \
+--class_names_file ./multilingual-sentiments/class_names.txt \
+--hypothesis_template "The sentiment of this text is {}." \
+--teacher_name_or_path MoritzLaurer/mDeBERTa-v3-base-mnli-xnli \
+--teacher_batch_size 32 \
+--student_name_or_path distilbert-base-multilingual-cased \
+--output_dir ./distilbert-base-multilingual-cased-sentiments-student \
+--per_device_train_batch_size 16 \
+--fp16
+```
+
+If you are training this model on Colab, make the following code changes to avoid Out-of-memory error message:
+```bash
+  ###### modify L78 to disable fast tokenizer 
+  default=False,
+
+  ###### update dataset map part at L313
+  dataset = dataset.map(tokenizer, input_columns="text", fn_kwargs={"padding": "max_length", "truncation": True, "max_length": 512})
+
+  ###### add following lines to L213
+  del model
+  print(f"Manually deleted Teacher model, free some memory for student model.")
+
+  ###### add following lines to L337
+  trainer.push_to_hub()
+  tokenizer.push_to_hub("distilbert-base-multilingual-cased-sentiments-student")
+  
+```
+
+### Training log
+```bash
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+{'train_runtime': 2009.8864, 'train_samples_per_second': 73.0, 'train_steps_per_second': 4.563, 'train_loss': 0.6473459283913797, 'epoch': 1.0}
+100%|███████████████████████████████████████| 9171/9171 [33:29<00:00,  4.56it/s]
+[INFO|trainer.py:762] 2023-05-06 10:56:18,555 >> The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3129] 2023-05-06 10:56:18,557 >> ***** Running Evaluation *****
+[INFO|trainer.py:3131] 2023-05-06 10:56:18,557 >>   Num examples = 146721
+[INFO|trainer.py:3134] 2023-05-06 10:56:18,557 >>   Batch size = 128
+100%|███████████████████████████████████████| 1147/1147 [08:59<00:00,  2.13it/s]
+05/06/2023 11:05:18 - INFO - __main__ - Agreement of student and teacher predictions: 88.29%
+[INFO|trainer.py:2868] 2023-05-06 11:05:18,251 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student
+[INFO|configuration_utils.py:457] 2023-05-06 11:05:18,251 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/config.json
+[INFO|modeling_utils.py:1847] 2023-05-06 11:05:18,905 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/pytorch_model.bin
+[INFO|tokenization_utils_base.py:2171] 2023-05-06 11:05:18,905 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2178] 2023-05-06 11:05:18,905 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/special_tokens_map.json
+
+```
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3