Mariaaaaa/best_model_with_bitfit
Browse files- README.md +64 -0
- config.json +26 -0
- model.safetensors +3 -0
- run-0/checkpoint-1250/config.json +26 -0
- run-0/checkpoint-1250/model.safetensors +3 -0
- run-0/checkpoint-1250/optimizer.pt +3 -0
- run-0/checkpoint-1250/rng_state.pth +3 -0
- run-0/checkpoint-1250/scheduler.pt +3 -0
- run-0/checkpoint-1250/trainer_state.json +51 -0
- run-0/checkpoint-1250/training_args.bin +3 -0
- run-0/checkpoint-2500/config.json +26 -0
- run-0/checkpoint-2500/model.safetensors +3 -0
- run-0/checkpoint-2500/optimizer.pt +3 -0
- run-0/checkpoint-2500/rng_state.pth +3 -0
- run-0/checkpoint-2500/scheduler.pt +3 -0
- run-0/checkpoint-2500/trainer_state.json +84 -0
- run-0/checkpoint-2500/training_args.bin +3 -0
- run-0/checkpoint-3750/config.json +26 -0
- run-0/checkpoint-3750/model.safetensors +3 -0
- run-0/checkpoint-3750/optimizer.pt +3 -0
- run-0/checkpoint-3750/rng_state.pth +3 -0
- run-0/checkpoint-3750/scheduler.pt +3 -0
- run-0/checkpoint-3750/trainer_state.json +117 -0
- run-0/checkpoint-3750/training_args.bin +3 -0
- run-0/checkpoint-5000/config.json +26 -0
- run-0/checkpoint-5000/model.safetensors +3 -0
- run-0/checkpoint-5000/optimizer.pt +3 -0
- run-0/checkpoint-5000/rng_state.pth +3 -0
- run-0/checkpoint-5000/scheduler.pt +3 -0
- run-0/checkpoint-5000/trainer_state.json +150 -0
- run-0/checkpoint-5000/training_args.bin +3 -0
- training_args.bin +3 -0
README.md
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---
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license: mit
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base_model: prajjwal1/bert-tiny
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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: checkpoints
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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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# checkpoints
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6530
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- Accuracy: 0.6392
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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: 0.0003
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- train_batch_size: 16
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- eval_batch_size: 8
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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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- lr_scheduler_warmup_steps: 500
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- num_epochs: 4
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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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| 0.6751 | 1.0 | 1250 | 0.6668 | 0.626 |
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| 0.6634 | 2.0 | 2500 | 0.6576 | 0.634 |
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| 0.6584 | 3.0 | 3750 | 0.6556 | 0.6286 |
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| 0.6548 | 4.0 | 5000 | 0.6530 | 0.6392 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "prajjwal1/bert-tiny",
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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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"hidden_act": "gelu",
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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": "4.37.2",
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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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model.safetensors
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run-0/checkpoint-1250/config.json
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"problem_type": "single_label_classification",
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run-0/checkpoint-1250/model.safetensors
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run-0/checkpoint-1250/optimizer.pt
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run-0/checkpoint-1250/rng_state.pth
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}
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run-0/checkpoint-1250/training_args.bin
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run-0/checkpoint-2500/config.json
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