transformers tensorboard safetensors mt5 text2text-generation generated_from_trainer base_model:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
- wer
base_model: google/mt5-small
model-index:
- name: nep-spell-mt5-small-0
  results: []
---

<!-- 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. -->

# nep-spell-mt5-small-0

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 0.8446
- Precision: 0.889
- Recall: 0.8446
- F1: 0.8603
- Wer: 0.0105
- Cer: 0.0024
- Chrf: 99.2925
- Exact Match: 0.8446
- Bertscore:precision: 0.9972
- Bertscore:recall: 0.9975
- Bertscore:f1: 0.9973
- Ter: 1.0546
- Blerurt: 0.8889

## 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: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Wer    | Cer    | Chrf    | Exact Match | Bertscore:precision | Bertscore:recall | Bertscore:f1 | Ter    | Blerurt |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|:-------:|:-----------:|:-------------------:|:----------------:|:------------:|:------:|:-------:|
| 0.006         | 0.75  | 10000 | 0.0009          | 0.8446   | 0.889     | 0.8446 | 0.8603 | 0.0105 | 0.0024 | 99.2925 | 0.8446      | 0.9972              | 0.9975           | 0.9973       | 1.0546 | 0.8889  |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
