Model save

README.md

@@ -1,57 +1,56 @@
 ---
-base_model: HuggingFaceTB/SmolLM2-135M
 library_name: transformers
-model_name: smol-135-bias-scorer
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M
 tags:
 - generated_from_trainer
-- trl
-- sft
-licence: license
+model-index:
+- name: bias-scorer-smollm2-135m
+  results: []
 ---
 
-# Model Card for smol-135-bias-scorer
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# bias-scorer-smollm2-135m
 
-## Quick start
+This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
 
-```python
-from transformers import pipeline
+## Model description
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="tcapelle/smol-135-bias-scorer", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+More information needed
 
-## Training procedure
+## Intended uses & limitations
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/c-metrics/toxicity-scorer-ft/runs/55ybbr8a)
+More information needed
 
-This model was trained with SFT.
+## Training and evaluation data
 
-### Framework versions
+More information needed
 
-- TRL: 0.12.1
-- Transformers: 4.46.3
-- Pytorch: 2.5.1
-- Datasets: 3.1.0
-- Tokenizers: 0.20.3
+## Training procedure
 
-## Citations
+### Training hyperparameters
 
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 2
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
+| No log        | 0     | 0    | 1.1728          | 0.5357 | 0.5      | 0.8676    | 0.5    |
+
+
+### Framework versions
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.3