Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mayank-01/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayank-01/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayank-01/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- .gitignore +1 -0
- config.json +25 -0
- pytorch_model.bin +3 -0
- runs/Jul28_10-41-42_0b9abdceccbc/1659004906.7978184/events.out.tfevents.1659004906.0b9abdceccbc.71.1 +3 -0
- runs/Jul28_10-41-42_0b9abdceccbc/events.out.tfevents.1659004906.0b9abdceccbc.71.0 +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
.gitignore
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checkpoint-*/
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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": "4.21.0",
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:5aeb371bf5f29df235d580993bc1454e780b87a96364ee4374562f93dad33c7c
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size 267854321
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runs/Jul28_10-41-42_0b9abdceccbc/1659004906.7978184/events.out.tfevents.1659004906.0b9abdceccbc.71.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:437adc7d4d4aa1acdeaf725903e850233508ff89e0c54d947e9606d0b65107ca
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size 5469
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runs/Jul28_10-41-42_0b9abdceccbc/events.out.tfevents.1659004906.0b9abdceccbc.71.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:ffb27c4e7322b1fb7b5e990026e5dc75d08ad946012ef994f942e5f37afdd3f8
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size 3597
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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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"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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"name_or_path": "distilbert-base-uncased",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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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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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:78fbf0dd2571cc2be5cb1856c52126c83e9b47823e8c81080bf172c6a8133e0d
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size 3375
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vocab.txt
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