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---
license: mit
tags:
- generated_from_trainer
datasets:
- esnli
metrics:
- f1
- accuracy
model-index:
- name: roberta-large-e-snli-classification-nli-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: esnli
type: esnli
config: plain_text
split: validation
args: plain_text
metrics:
- name: F1
type: f1
value: 0.9258678577111056
- name: Accuracy
type: accuracy
value: 0.9260312944523471
---
<!-- 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-large-e-snli-classification-nli-base
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2221
- F1: 0.9259
- Accuracy: 0.9260
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.9995 | 0.05 | 400 | 0.4236 | 0.8437 | 0.8465 |
| 0.4089 | 0.09 | 800 | 0.2961 | 0.8926 | 0.8933 |
| 0.3681 | 0.14 | 1200 | 0.2980 | 0.8914 | 0.8924 |
| 0.3467 | 0.19 | 1600 | 0.2872 | 0.8977 | 0.8990 |
| 0.324 | 0.23 | 2000 | 0.2506 | 0.9106 | 0.9110 |
| 0.3222 | 0.28 | 2400 | 0.2552 | 0.9132 | 0.9128 |
| 0.3138 | 0.33 | 2800 | 0.2379 | 0.9183 | 0.9183 |
| 0.3107 | 0.37 | 3200 | 0.2396 | 0.9152 | 0.9156 |
| 0.304 | 0.42 | 3600 | 0.2354 | 0.9174 | 0.9177 |
| 0.3027 | 0.47 | 4000 | 0.2360 | 0.9191 | 0.9191 |
| 0.2968 | 0.51 | 4400 | 0.2329 | 0.9182 | 0.9187 |
| 0.2888 | 0.56 | 4800 | 0.2462 | 0.9189 | 0.9196 |
| 0.2898 | 0.61 | 5200 | 0.2335 | 0.9206 | 0.9212 |
| 0.288 | 0.65 | 5600 | 0.2350 | 0.9220 | 0.9223 |
| 0.2746 | 0.7 | 6000 | 0.2208 | 0.9275 | 0.9278 |
| 0.2756 | 0.75 | 6400 | 0.2304 | 0.9209 | 0.9216 |
| 0.272 | 0.79 | 6800 | 0.2243 | 0.9237 | 0.9238 |
| 0.2809 | 0.84 | 7200 | 0.2176 | 0.9259 | 0.9261 |
| 0.2733 | 0.89 | 7600 | 0.2194 | 0.9271 | 0.9273 |
| 0.2723 | 0.93 | 8000 | 0.2221 | 0.9259 | 0.9260 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
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