doyoungkim
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add model
Browse files- .gitignore +1 -0
- README.md +62 -0
- config.json +27 -0
- pytorch_model.bin +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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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model_index:
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name: bert-base-uncased-sst2-distilled
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-base-uncased-sst2-distilled
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2676
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- Accuracy: 0.9025
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.3797 | 1.0 | 2105 | 0.2512 | 0.9002 |
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| 0.3036 | 2.0 | 4210 | 0.2643 | 0.8933 |
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| 0.2609 | 3.0 | 6315 | 0.2831 | 0.8956 |
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| 0.2417 | 4.0 | 8420 | 0.2676 | 0.9025 |
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| 0.2305 | 5.0 | 10525 | 0.2740 | 0.9025 |
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### Framework versions
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- Transformers 4.9.1
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- Pytorch 1.8.1
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- Datasets 1.11.0
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- Tokenizers 0.10.1
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config.json
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"MyBertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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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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"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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"pretrained_model_name_or_path": "bert-base-uncased",
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"problem_type": "multi_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.9.1",
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"type_vocab_size": 2,
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"use_cache": true,
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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:e639335b5471b3c820b6115f6c3a60a64abf9c0189936a7bff4b571b78e6601d
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size 438024457
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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": true, "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-uncased", "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:af463ca2e12d3fcfdfa6965f643681592d41f984f0a6dfe146f94b3b18f34f3b
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size 2671
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vocab.txt
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