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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-downstream-ildc
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-base-downstream-ildc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7039
- Accuracy: 0.5030
- Precision: 0.5015
- Recall: 0.9960
- F1: 0.6671
- Best Threshold: 0.4007
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Best Threshold |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------:|
| 0.6863 | 1.0 | 1010 | 0.7004 | 0.5111 | 0.5057 | 0.9859 | 0.6685 | 0.4378 |
| 0.6812 | 2.0 | 2020 | 0.6994 | 0.5030 | 0.5015 | 0.9960 | 0.6671 | 0.4333 |
| 0.6816 | 3.0 | 3030 | 0.7515 | 0.5030 | 0.5015 | 0.9839 | 0.6644 | 0.3329 |
| 0.6796 | 4.0 | 4040 | 0.7039 | 0.5030 | 0.5015 | 0.9960 | 0.6671 | 0.4007 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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