lora-roberta-large-finetuned-reduced_captures

README.md

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+---
+license: mit
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: roberta-large
+metrics:
+- accuracy
+model-index:
+- name: lora-roberta-large-finetuned-reduced_captures
+  results: []
+---
+
+<!-- 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. -->
+
+# lora-roberta-large-finetuned-reduced_captures
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2458
+- Accuracy: 0.9345
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 10
+- eval_batch_size: 10
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 40
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.3924        | 0.9996 | 616  | 0.3862          | 0.8906   |
+| 0.4385        | 1.9992 | 1232 | 0.3267          | 0.9048   |
+| 0.388         | 2.9988 | 1848 | 0.2849          | 0.9175   |
+| 0.3256        | 4.0    | 2465 | 0.2728          | 0.9207   |
+| 0.2718        | 4.9996 | 3081 | 0.2939          | 0.9170   |
+| 0.2877        | 5.9992 | 3697 | 0.2522          | 0.9267   |
+| 0.233         | 6.9988 | 4313 | 0.2624          | 0.9260   |
+| 0.1832        | 8.0    | 4930 | 0.2512          | 0.9317   |
+| 0.2399        | 8.9996 | 5546 | 0.2458          | 0.9345   |
+| 0.1506        | 9.9959 | 6160 | 0.2390          | 0.9336   |
+
+
+### Framework versions
+
+- PEFT 0.12.0
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.0

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