prompt_fine_tuned_rte_XLMroberta
Browse files- .gitattributes +1 -0
- README.md +71 -0
- adapter_config.json +17 -0
- adapter_model.safetensors +3 -0
- runs/May23_08-41-54_2e329e5d4728/events.out.tfevents.1716453715.2e329e5d4728.4382.0 +3 -0
- runs/May23_08-41-54_2e329e5d4728/events.out.tfevents.1716453757.2e329e5d4728.4382.1 +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +15 -0
- tokenizer.json +3 -0
- tokenizer_config.json +54 -0
- training_args.bin +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: xlm-roberta-base
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metrics:
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- accuracy
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- f1
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model-index:
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- name: prompt_fine_tuned_rte_XLMroberta
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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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# prompt_fine_tuned_rte_XLMroberta
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7052
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- Accuracy: 0.3448
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- F1: 0.3399
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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: 8
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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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- training_steps: 400
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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| 0.6899 | 1.7241 | 50 | 0.7067 | 0.4138 | 0.3957 |
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| 0.7066 | 3.4483 | 100 | 0.7054 | 0.4483 | 0.4221 |
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| 0.6996 | 5.1724 | 150 | 0.7054 | 0.4483 | 0.4221 |
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| 0.6876 | 6.8966 | 200 | 0.7056 | 0.4138 | 0.3957 |
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| 0.699 | 8.6207 | 250 | 0.7051 | 0.4138 | 0.3957 |
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| 0.6936 | 10.3448 | 300 | 0.7055 | 0.3448 | 0.3399 |
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| 0.6909 | 12.0690 | 350 | 0.7052 | 0.3448 | 0.3399 |
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| 0.69 | 13.7931 | 400 | 0.7052 | 0.3448 | 0.3399 |
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.41.0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "xlm-roberta-base",
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"inference_mode": true,
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"num_attention_heads": 12,
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"num_layers": 12,
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"num_transformer_submodules": 1,
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"num_virtual_tokens": 12,
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"peft_type": "PROMPT_TUNING",
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"prompt_tuning_init": "RANDOM",
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"prompt_tuning_init_text": null,
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"revision": null,
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"task_type": "SEQ_CLS",
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"token_dim": 768,
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"tokenizer_kwargs": null,
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"tokenizer_name_or_path": "xlm-roberta-base"
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2405904
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runs/May23_08-41-54_2e329e5d4728/events.out.tfevents.1716453715.2e329e5d4728.4382.0
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sentencepiece.bpe.model
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special_tokens_map.json
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{
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tokenizer.json
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size 17082734
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tokenizer_config.json
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training_args.bin
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