transformers pytorch tensorboard albert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: albert-large-v2-finetuned-mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.8872549019607843
      name: Accuracy
    - type: f1
      value: 0.9190140845070423
      name: F1
---

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# albert-large-v2-finetuned-mrpc

This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4449
- Accuracy: 0.8873
- F1: 0.9190

## 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: 32
- eval_batch_size: 32
- seed: 71
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 115  | 0.3615          | 0.8456   | 0.8844 |
| No log        | 2.0   | 230  | 0.2958          | 0.8824   | 0.9155 |
| No log        | 3.0   | 345  | 0.3497          | 0.8701   | 0.9081 |
| No log        | 4.0   | 460  | 0.4280          | 0.8725   | 0.9100 |
| 0.2707        | 5.0   | 575  | 0.4449          | 0.8873   | 0.9190 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
