transformers pytorch roberta text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: randomcomb_mlm_ep5_mnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - type: accuracy
      value: 0.8615744507729862
      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. -->

# randomcomb_mlm_ep5_mnli

This model is a fine-tuned version of [cuenb](https://huggingface.co/joey234/cuenb) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4416
- Accuracy: 0.8616

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5569        | 0.41  | 5000  | 0.4415          | 0.8273   |
| 0.4598        | 0.81  | 10000 | 0.4234          | 0.8425   |
| 0.3832        | 1.22  | 15000 | 0.4398          | 0.8475   |
| 0.3314        | 1.63  | 20000 | 0.4137          | 0.8494   |
| 0.3158        | 2.04  | 25000 | 0.4484          | 0.8527   |
| 0.2294        | 2.44  | 30000 | 0.4471          | 0.8552   |
| 0.2283        | 2.85  | 35000 | 0.4541          | 0.8557   |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.8.0
- Datasets 1.18.3
- Tokenizers 0.12.1
