End of training
Browse files- README.md +12 -13
- config.json +17 -15
- model.safetensors +2 -2
- runs/Feb19_10-32-50_f8343483f830/events.out.tfevents.1708339279.f8343483f830.190.1 +3 -0
- runs/Feb19_10-44-49_f8343483f830/events.out.tfevents.1708339490.f8343483f830.190.2 +3 -0
- special_tokens_map.json +5 -35
- tokenizer_config.json +0 -7
- training_args.bin +1 -1
README.md
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---
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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@@ -18,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
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# sentence-classifiert
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- Accuracy: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
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| No log | 1.0 | 154 | 0.
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| No log | 2.0 | 308 | 0.
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| No log | 3.0 | 462 | 0.
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### Framework versions
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---
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base_model: dmis-lab/biobert-v1.1
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tags:
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- generated_from_trainer
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metrics:
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# sentence-classifiert
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3410
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- Precision: 0.9085
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- Recall: 0.9068
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- Accuracy: 0.9072
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- F1: 0.9072
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
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| No log | 1.0 | 154 | 0.4158 | 0.8549 | 0.8445 | 0.8438 | 0.8443 |
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| No log | 2.0 | 308 | 0.3426 | 0.8875 | 0.8804 | 0.8796 | 0.8787 |
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| No log | 3.0 | 462 | 0.3594 | 0.8945 | 0.8856 | 0.8869 | 0.8868 |
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| 0.3638 | 4.0 | 616 | 0.3302 | 0.9034 | 0.9008 | 0.9015 | 0.9014 |
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| 0.3638 | 5.0 | 770 | 0.3410 | 0.9085 | 0.9068 | 0.9072 | 0.9072 |
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### Framework versions
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config.json
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{
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"_name_or_path": "
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"activation": "gelu",
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"architectures": [
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"
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],
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"
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"
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"
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"id2label": {
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"0": "marker/mechanism",
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"1": "therapeutic",
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"2": "unrelated"
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},
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"initializer_range": 0.02,
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"label2id": {
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"marker/mechanism": 0,
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"therapeutic": 1,
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"unrelated": 2
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},
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"max_position_embeddings": 512,
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"model_type": "
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"
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"
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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": "4.35.2",
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"
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}
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{
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"_name_or_path": "dmis-lab/biobert-v1.1",
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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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"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": "marker/mechanism",
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"1": "therapeutic",
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"2": "unrelated"
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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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"marker/mechanism": 0,
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"therapeutic": 1,
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"unrelated": 2
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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": "4.35.2",
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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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model.safetensors
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runs/Feb19_10-32-50_f8343483f830/events.out.tfevents.1708339279.f8343483f830.190.1
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runs/Feb19_10-44-49_f8343483f830/events.out.tfevents.1708339490.f8343483f830.190.2
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special_tokens_map.json
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"cls_token":
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"single_word": false
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"pad_token": {
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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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"unk_token": {
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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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}
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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_config.json
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"do_basic_tokenize": true,
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"do_lower_case": false,
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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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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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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"do_basic_tokenize": true,
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"do_lower_case": false,
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training_args.bin
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