dingusagar
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finetuned model files
Browse files- Readme.md +34 -0
- config.json +25 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +55 -0
- vocab.txt +0 -0
Readme.md
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# DistilBERT Fine-Tuned for Sequence Classification
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## Model Overview
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This is a fine-tuned version of the DistilBERT model designed for sequence classification tasks. It is inspired by the r/AmItheAsshole subreddit, where it has been trained on textual data to assess and classify user-submitted stories.
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- **Base Model**: [DistilBERT](https://huggingface.co/distilbert-base-uncased)
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- **Fine-Tuned For**: Sequence classification (e.g., sentiment analysis, AITA-type categorization)
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- **Dataset**: https://huggingface.co/datasets/MattBoraske/Reddit-AITA-2018-to-2022
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- **Task**: Sequence classification with predefined labels.
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## Model Details
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- **Architecture**: Transformer-based model (DistilBERT)
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- **Input Format**: Text sequences
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- **Output Format**: Classification labels with confidence scores
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- **Labels**:
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- `LABEL_0`: The Asshole
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- `LABEL_1`: Not the Asshole
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## Intended Use
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This model is intended to provide insights and assessments for user-submitted textual scenarios. It works well for binary classification tasks.
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### Example Usage
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```python
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from transformers import pipeline
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classifier = pipeline(
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"text-classification",
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model="your-username/your-model-name"
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)
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text = "I did not invite my friend for my wedding. AITA ?"
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result = classifier(text)
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print(result)
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config.json
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"vocab_size": 30522
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c9f7a0f7c4e0b2cc8ad4a3c428776bee1a0f56bade8fd9b65a27db827c0b254b
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size 267832560
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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