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---
license: mit
tags:
- generated_from_trainer
datasets:
- esnli
metrics:
- f1
- accuracy
model-index:
- name: roberta-base-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: 0.9108298866502319
- name: Accuracy
type: accuracy
value: 0.9109937004673847
---
<!-- 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-e-snli-classification-nli-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2611
- F1: 0.9108
- Accuracy: 0.9110
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 1.0317 | 0.05 | 400 | 0.5734 | 0.7771 | 0.7803 |
| 0.544 | 0.09 | 800 | 0.3994 | 0.8548 | 0.8555 |
| 0.4604 | 0.14 | 1200 | 0.3492 | 0.8681 | 0.8687 |
| 0.4235 | 0.19 | 1600 | 0.3323 | 0.8764 | 0.8777 |
| 0.3934 | 0.23 | 2000 | 0.3225 | 0.8831 | 0.8841 |
| 0.3863 | 0.28 | 2400 | 0.3086 | 0.8875 | 0.8872 |
| 0.3767 | 0.33 | 2800 | 0.2972 | 0.8892 | 0.8898 |
| 0.3726 | 0.37 | 3200 | 0.2910 | 0.8932 | 0.8936 |
| 0.3624 | 0.42 | 3600 | 0.2934 | 0.8934 | 0.8937 |
| 0.361 | 0.47 | 4000 | 0.2831 | 0.8989 | 0.8989 |
| 0.3553 | 0.51 | 4400 | 0.2905 | 0.8985 | 0.8993 |
| 0.3451 | 0.56 | 4800 | 0.2725 | 0.9019 | 0.9024 |
| 0.3475 | 0.61 | 5200 | 0.2712 | 0.9046 | 0.9051 |
| 0.3398 | 0.65 | 5600 | 0.2787 | 0.9024 | 0.9028 |
| 0.3322 | 0.7 | 6000 | 0.2697 | 0.9043 | 0.9046 |
| 0.3288 | 0.75 | 6400 | 0.2722 | 0.9006 | 0.9013 |
| 0.324 | 0.79 | 6800 | 0.2677 | 0.9066 | 0.9066 |
| 0.3335 | 0.84 | 7200 | 0.2629 | 0.9075 | 0.9077 |
| 0.3309 | 0.89 | 7600 | 0.2577 | 0.9058 | 0.9061 |
| 0.3236 | 0.93 | 8000 | 0.2561 | 0.9121 | 0.9121 |
| 0.3183 | 0.98 | 8400 | 0.2556 | 0.9084 | 0.9088 |
| 0.3022 | 1.03 | 8800 | 0.2668 | 0.9056 | 0.9064 |
| 0.2974 | 1.07 | 9200 | 0.2519 | 0.9087 | 0.9092 |
| 0.29 | 1.12 | 9600 | 0.2554 | 0.9103 | 0.9109 |
| 0.2855 | 1.16 | 10000 | 0.2611 | 0.9108 | 0.9110 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
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