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--- |
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base_model: mistralai/Mistral-7B-v0.1 |
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library_name: peft |
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license: apache-2.0 |
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tags: |
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- axolotl |
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- generated_from_trainer |
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model-index: |
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- name: source_of_injury |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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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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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.1` |
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```yaml |
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base_model: mistralai/Mistral-7B-v0.1 |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_mistral_derived_model: true |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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lora_fan_in_fan_out: false |
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data_seed: 49 |
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seed: 49 |
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datasets: |
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- path: NimaZahedinameghi/source_injury |
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type: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.1 |
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output_dir: ./qlora-alpaca-out |
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hub_model_id: NimaZahedinameghi/source_of_injury |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 896 |
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sample_packing: false |
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pad_to_sequence_len: true |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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lora_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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wandb_project: source_of_injury |
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wandb_entity: uqam |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 16 |
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eval_batch_size: 16 |
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num_epochs: 3 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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max_grad_norm: 1.0 |
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adam_beta2: 0.95 |
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adam_epsilon: 0.00001 |
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save_total_limit: 12 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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warmup_steps: 20 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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saves_per_epoch: 6 |
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debug: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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save_safetensors: true |
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``` |
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</details><br> |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/uqam/source_of_injury/runs/2q7kaw2k) |
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# source_of_injury |
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This model is built by [Nima Zahedinameghi](https://www.linkedin.com/in/nima-zahedi-nameghi-ph-d-3b7061146/). It's a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1) on the [source_injury](https://huggingface.co./datasets/NimaZahedinameghi/source_injury) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5867, after 3 epochs. |
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**Important:** The model is still underdeveloped. It has been trained on a tiny synthetic data with limited medical labels. It's capabilities are limited. I will release the new version of the model soon. |
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## How to use the model |
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1. **Pip install the required dependencies** |
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``` txt |
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transformers==4.36.2 |
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datasets==2.15.0 |
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peft==0.6.0 |
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accelerate==0.24.1 |
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bitsandbytes==0.41.3.post2 |
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safetensors==0.4.1 |
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scipy==1.11.4 |
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sentencepiece==0.1.99 |
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protobuf==4.23.4 --upgrade |
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``` |
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2. **Load the Model and Tokenizer:** |
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```python |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer |
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model_id = 'NimaZahedinameghi/source_of_injury' |
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model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda() |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizer.pad_token = tokenizer.eos_token |
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``` |
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3. **Define the Prompt Function:** |
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Create a function to structure your prompt correctly: |
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```python |
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def prompt(incident_description): |
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return f"""[INST] <<SYS>> |
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The claims are managed by reviewing incident descriptions submitted by workers. Claim coders review the incident description and populate a database with reasoning towards determining the source of injury (InjurySource). |
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<</SYS>> |
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IncidentDescription: {incident_description} |
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[/INST] |
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""" |
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def prompt_tok(incident_description): |
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_p = prompt(incident_description) |
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input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda() |
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out_ids = model.generate(input_ids=input_ids, max_new_tokens=500, do_sample=False) |
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return tokenizer.batch_decode(out_ids.detach().cpu().numpy(), skip_special_tokens=True)[0][len(_p):] |
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``` |
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4. **Make Predictions:** |
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Use the function to get predictions from your model: |
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```python |
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incident_description = "While working on a vehicle repair, I had to contort my body to access hard-to-reach areas. This position caused severe discomfort and pain in my neck and shoulders." |
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output = prompt_tok(incident_description) |
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print(output) |
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``` |
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This function will take an incident description and return the reasoning and injury source as determined by your fine-tuned model. Ensure you follow the specific prompt format that matches your training setup. |
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## Model description |
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the model is fine tuned on a small synthetic dataset with 4bit precision. The model is still underdeveloped and covers very limited number of medical labels. |
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## Intended uses & limitations |
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Further testing is required to evaluate the model performance on custome evaluations |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 49 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.0189 | 0.1481 | 1 | 2.0511 | |
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| 2.0285 | 0.2963 | 2 | 2.0442 | |
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| 1.9559 | 0.5926 | 4 | 1.9259 | |
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| 1.802 | 0.8889 | 6 | 1.6212 | |
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| 1.4115 | 1.1852 | 8 | 1.2261 | |
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| 1.1171 | 1.4815 | 10 | 1.0004 | |
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| 0.9691 | 1.7778 | 12 | 0.8657 | |
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| 0.747 | 2.0741 | 14 | 0.7082 | |
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| 0.6407 | 2.3704 | 16 | 0.6205 | |
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| 0.6101 | 2.6667 | 18 | 0.5867 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
