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---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-Reflections-goodareas-eval_FeedbackESConv5pp_CARE10pp-sweeps-current
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. -->
# roberta-Reflections-goodareas-eval_FeedbackESConv5pp_CARE10pp-sweeps-current
This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4328
- Accuracy: 0.8678
- Precision: 0.4273
- Recall: 0.5402
- F1: 0.4772
## 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: 9.49118803819061e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3453 | 1.0 | 80 | 0.2368 | 0.8845 | 0.4444 | 0.1379 | 0.2105 |
| 0.2686 | 2.0 | 160 | 0.1995 | 0.8883 | 0.0 | 0.0 | 0.0 |
| 0.2467 | 3.0 | 240 | 0.2582 | 0.8755 | 0.4561 | 0.5977 | 0.5174 |
| 0.2346 | 4.0 | 320 | 0.1663 | 0.9012 | 0.6923 | 0.2069 | 0.3186 |
| 0.2153 | 5.0 | 400 | 0.1441 | 0.9037 | 0.8 | 0.1839 | 0.2991 |
| 0.2003 | 6.0 | 480 | 0.2784 | 0.8267 | 0.3571 | 0.6897 | 0.4706 |
| 0.1806 | 7.0 | 560 | 0.1637 | 0.8999 | 0.5495 | 0.5747 | 0.5618 |
| 0.1477 | 8.0 | 640 | 0.2062 | 0.8639 | 0.4275 | 0.6437 | 0.5138 |
| 0.1234 | 9.0 | 720 | 0.2175 | 0.8626 | 0.4167 | 0.5747 | 0.4831 |
| 0.1116 | 10.0 | 800 | 0.1914 | 0.8845 | 0.4810 | 0.4368 | 0.4578 |
| 0.0959 | 11.0 | 880 | 0.3313 | 0.8485 | 0.3916 | 0.6437 | 0.4870 |
| 0.0933 | 12.0 | 960 | 0.3027 | 0.8575 | 0.4048 | 0.5862 | 0.4789 |
| 0.0796 | 13.0 | 1040 | 0.3267 | 0.8575 | 0.4032 | 0.5747 | 0.4739 |
| 0.0688 | 14.0 | 1120 | 0.2958 | 0.8819 | 0.4731 | 0.5057 | 0.4889 |
| 0.0723 | 15.0 | 1200 | 0.4122 | 0.8575 | 0.4032 | 0.5747 | 0.4739 |
| 0.048 | 16.0 | 1280 | 0.5274 | 0.8447 | 0.3851 | 0.6552 | 0.4851 |
| 0.0504 | 17.0 | 1360 | 0.5241 | 0.8562 | 0.4031 | 0.5977 | 0.4815 |
| 0.0353 | 18.0 | 1440 | 0.4845 | 0.8601 | 0.4098 | 0.5747 | 0.4785 |
| 0.0485 | 19.0 | 1520 | 0.5141 | 0.8562 | 0.4031 | 0.5977 | 0.4815 |
| 0.0481 | 20.0 | 1600 | 0.4328 | 0.8678 | 0.4273 | 0.5402 | 0.4772 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0
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