Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sgugger/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/bert-finetuned-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("sgugger/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- .gitignore +1 -0
- config.json +36 -0
- pytorch_model.bin +3 -0
- runs/Sep14_13-08-06_brahms/1631639298.2348537/events.out.tfevents.1631639298.brahms.1547751.1 +3 -0
- runs/Sep14_13-08-06_brahms/events.out.tfevents.1631639298.brahms.1547751.0 +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -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": "bert-base-cased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"finetuning_task": "mrpc",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "not_equivalent",
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"1": "equivalent"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"equivalent": 1,
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"not_equivalent": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.11.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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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 sha256:0beca7a93620540c192c3c099865c02bdccf4f9060746097d4542ee1fee997b7
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size 433336585
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runs/Sep14_13-08-06_brahms/1631639298.2348537/events.out.tfevents.1631639298.brahms.1547751.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e1fe7dfdc3d47498e2b6700e0d7d90230caf57c627247a94e4f262892b9d95d
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size 4379
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runs/Sep14_13-08-06_brahms/events.out.tfevents.1631639298.brahms.1547751.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:f13e4797d34fa59b90568161cb8544a05730ec66233286e020a5b93ff8b9f926
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size 3686
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-cased", "tokenizer_class": "BertTokenizer"}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:151cdca9dff48a6afefccb63b9dc83843a5a1f93050673d5a133ac22a11db41a
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size 2799
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vocab.txt
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