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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- sagnikrayc/snli-cf-kaushik
metrics:
- accuracy
model-index:
- name: roberta-base-fp-sick
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: snli-cf-kaushik
type: sagnikrayc/snli-cf-kaushik
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4395
---
<!-- 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-fp-sick
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the snli-cf-kaushik dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1373
- Accuracy: 0.4395
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 70 | 0.4164 | 0.8485 |
| No log | 2.0 | 140 | 0.3497 | 0.8747 |
| No log | 3.0 | 210 | 0.3346 | 0.8727 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|