Finished training.
Browse files- README.md +81 -0
- adapter_config.json +27 -0
- adapter_model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- parquet
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- text-classification
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datasets:
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- tweet_eval
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metrics:
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- accuracy
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base_model: Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two
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model-index:
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- name: Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: tweet_eval
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type: tweet_eval
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config: emotion
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split: validation
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args: emotion
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metrics:
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- type: accuracy
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value: 0.7352941176470589
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name: accuracy
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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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# Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion
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This model is a fine-tuned version of [Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two](https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two) on the tweet_eval dataset.
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It achieves the following results on the evaluation set:
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- accuracy: 0.7353
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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: 0.0004
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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: 4
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### Training results
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| accuracy | train_loss | epoch |
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|:--------:|:----------:|:-----:|
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| 0.4037 | None | 0 |
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| 0.5160 | 1.2275 | 0 |
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| 0.6979 | 0.9809 | 1 |
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| 0.7193 | 0.8033 | 2 |
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| 0.7353 | 0.7538 | 3 |
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### Framework versions
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- PEFT 0.8.2
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- Transformers 4.37.2
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- Pytorch 2.2.0
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- Datasets 2.16.1
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- Tokenizers 0.15.2
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 1,
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"lora_dropout": 0.1,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 1,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"query",
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"value"
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],
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"task_type": "SEQ_CLS",
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"use_rslora": false
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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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oid sha256:42f607e1d091eea1d9e6e05d6f9d01db22d24385da4ed2ece1b2cbed538bbdef
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size 166552
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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": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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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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"never_split": null,
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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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