End of training
Browse files- README.md +65 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -0
- vocab.txt +0 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: google-bert/bert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- f1
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model-index:
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- name: modernbert-llm-router
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results: []
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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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# modernbert-llm-router
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2682
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- F1: 0.9335
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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: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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 | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 1.5713 | 1.0 | 313 | 1.1430 | 0.8117 |
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| 0.5179 | 2.0 | 626 | 0.4525 | 0.9143 |
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| 0.2474 | 3.0 | 939 | 0.3235 | 0.9243 |
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| 0.134 | 4.0 | 1252 | 0.2804 | 0.9320 |
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| 0.0724 | 5.0 | 1565 | 0.2682 | 0.9335 |
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### Framework versions
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- Transformers 4.48.0.dev0
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.21.0
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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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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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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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"special": true
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},
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"100": {
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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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"special": true
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},
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"101": {
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"content": "[CLS]",
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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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"special": true
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},
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"102": {
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"content": "[SEP]",
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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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"special": true
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},
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"103": {
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"content": "[MASK]",
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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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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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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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vocab.txt
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