Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use emrevarol/dz_finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrevarol/dz_finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrevarol/dz_finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emrevarol/dz_finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("emrevarol/dz_finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- .gitignore +1 -0
- config.json +25 -0
- pytorch_model.bin +3 -0
- runs/Nov01_12-05-13_cf3ee34b46ee/1667304333.6918492/events.out.tfevents.1667304333.cf3ee34b46ee.169.1 +3 -0
- runs/Nov01_12-05-13_cf3ee34b46ee/events.out.tfevents.1667304333.cf3ee34b46ee.169.0 +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": "4.23.1",
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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 sha256:2e8fbebee0f394e8a430928061e8f528e61d3f2fa1ae02df4709ca66c7492142
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size 267854321
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runs/Nov01_12-05-13_cf3ee34b46ee/1667304333.6918492/events.out.tfevents.1667304333.cf3ee34b46ee.169.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:73fffdece62514473cce7a2bd42bee0461f95fafcc6797afe66e083308888deb
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size 5537
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runs/Nov01_12-05-13_cf3ee34b46ee/events.out.tfevents.1667304333.cf3ee34b46ee.169.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:81cdce9554ea704c9fb3498b83a6f164433f44e11cc163faa255a4facd24781b
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size 40
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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 sha256:24628843e4d72d188047c23be7d51f631e45b3fd5b366cc34f4a5c7653ce5f51
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size 3439
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vocab.txt
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