metadata

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

distilbert-base-multilingual-cased-sentiments-student

Note

This is a fork of the distilbert-base-multilingual-cased-sentiments-student model. The original model card can be found here. This is just a conversion of the model to the ONNX format so it can be used in JavaScript/TypeScript applications.

This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment dataset using this script.

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

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: here

Training hyperparameters

Result can be reproduce using the following commands:

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:

###### 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


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