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---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: roberta-base
model-index:
- name: roberta-base-sst2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- type: accuracy
value: 0.9357798165137615
name: Accuracy
---
<!-- 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-sst2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2314
- Accuracy: 0.9358
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2287 | 1.0 | 4210 | 0.2314 | 0.9358 |
| 0.1959 | 2.0 | 8420 | 0.3027 | 0.9266 |
| 0.1635 | 3.0 | 12630 | 0.3022 | 0.9300 |
| 0.1148 | 4.0 | 16840 | 0.3162 | 0.9289 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|