Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ViktorDo/SciBERT-WIKI_Climber_Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/SciBERT-WIKI_Climber_Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/SciBERT-WIKI_Climber_Finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/SciBERT-WIKI_Climber_Finetuned") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/SciBERT-WIKI_Climber_Finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files- .gitignore +1 -0
- config.json +26 -0
- pytorch_model.bin +3 -0
- runs/Oct31_06-37-12_09a23e4ff0f0/1667198247.9961262/events.out.tfevents.1667198247.09a23e4ff0f0.76.1 +3 -0
- runs/Oct31_06-37-12_09a23e4ff0f0/events.out.tfevents.1667198247.09a23e4ff0f0.76.0 +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -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": "allenai/scibert_scivocab_uncased",
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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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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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": "4.23.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31090
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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 sha256:82439976c695f874d43f462a695dcd1edfa034de3837984a61afed931fa68cd1
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size 439751021
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runs/Oct31_06-37-12_09a23e4ff0f0/1667198247.9961262/events.out.tfevents.1667198247.09a23e4ff0f0.76.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:958065365a6b3bd29b9470dcf708bd43e9e277bf50077a1715830360238c92eb
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size 5508
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runs/Oct31_06-37-12_09a23e4ff0f0/events.out.tfevents.1667198247.09a23e4ff0f0.76.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:22a250dce0346db997a6b1d57771785bea975b77aeac7306f3bbccb8fa07d7e6
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size 3824
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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_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"name_or_path": "allenai/scibert_scivocab_uncased",
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"never_split": null,
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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": "BertTokenizer",
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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 sha256:8fd4983166aa89bbd009c7ed2a96b6aa98482674325a92b9bd888a8f46d487c5
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size 3439
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vocab.txt
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