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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
- f1
model-index:
- name: bert-multirc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: super_glue
type: super_glue
config: multirc
split: validation
args: multirc
metrics:
- name: Accuracy
type: accuracy
value: 0.574463696369637
- name: F1
type: f1
value: 0.5000357077611722
---
<!-- 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. -->
# bert-multirc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6812
- Accuracy: 0.5745
- F1: 0.5000
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6862 | 1.0 | 1703 | 0.6812 | 0.5745 | 0.5000 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|