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Sent_analysis_CVs

Text Classification
Transformers
PyTorch
Safetensors
distilbert
sentiment-analysis
zero-shot-distillation
distillation
zero-shot-classification
debarta-v3
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metadata
license: apache-2.0
tags:
  - sentiment-analysis
  - text-classification
  - zero-shot-distillation
  - distillation
  - zero-shot-classification
  - debarta-v3
model-index:
  - name: Softechlb/Sent_analysis_CVs
    results: []
datasets:
  - tyqiangz/multilingual-sentiments
language:
  - en
  - ar
  - de
  - es
  - fr
  - ja
  - zh
  - id
  - hi
  - it
  - ms
  - pt

Softechlb/Sent_analysis_CVs

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="Softechlb/Sent_analysis_CVs", 
    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("็งใฏใ“ใฎๆ˜ ็”ปใŒๅคงๅฅฝใใงใ€ไฝ•ๅบฆใ‚‚่ฆ‹ใพใ™๏ผ")
>> [[{'label': 'positive', 'score': 0.9342429041862488},
  {'label': 'neutral', 'score': 0.040193185210227966},
  {'label': 'negative', 'score': 0.025563929229974747}]]



### 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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