vedica1011
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vedica1011/distilbert-base-uncased-lora-text-classification
Browse files- README.md +63 -3
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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-
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---
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## Training procedure
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###
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---
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license: apache-2.0
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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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- accuracy
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model-index:
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- name: distilbert-base-uncased-lora-text-classification
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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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# distilbert-base-uncased-lora-text-classification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9997
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- Accuracy: {'accuracy': 0.882}
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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: 0.001
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 10
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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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| No log | 1.0 | 250 | 0.3444 | {'accuracy': 0.888} |
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| 0.4135 | 2.0 | 500 | 0.4854 | {'accuracy': 0.887} |
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| 0.4135 | 3.0 | 750 | 0.6411 | {'accuracy': 0.882} |
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| 0.2383 | 4.0 | 1000 | 0.6366 | {'accuracy': 0.891} |
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| 0.2383 | 5.0 | 1250 | 0.7062 | {'accuracy': 0.891} |
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| 0.1144 | 6.0 | 1500 | 0.7646 | {'accuracy': 0.882} |
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| 0.1144 | 7.0 | 1750 | 0.9373 | {'accuracy': 0.884} |
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| 0.0176 | 8.0 | 2000 | 1.0347 | {'accuracy': 0.884} |
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| 0.0176 | 9.0 | 2250 | 0.9923 | {'accuracy': 0.883} |
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| 0.0188 | 10.0 | 2500 | 0.9997 | {'accuracy': 0.882} |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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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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"add_prefix_space": true,
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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": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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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": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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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:c009b77fa6bcab075dd3aa9815086f3751c8641239fff20e95c52e850e654fe8
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size 4600
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vocab.txt
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