---
license: mit
base_model: microsoft/xtremedistil-l12-h384-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finer-139-xtremedistil-l12-h384
  results: []
datasets:
- nlpaueb/finer-139
---

<!-- 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. -->

# finer-139-xtremedistil-l12-h384

This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the [finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0133
- Precision: 0.6104
- Recall: 0.6581
- F1: 0.6334
- Accuracy: 0.9961

## Model description

Base model: microsoft/xtremedistil-l12-h384-uncased

## 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: 256
- eval_batch_size: 512
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 512
- total_eval_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0438        | 1.0   | 1759 | 0.0389          | 0.4777    | 0.1593 | 0.2389 | 0.9937   |
| 0.0266        | 2.0   | 3518 | 0.0234          | 0.5432    | 0.4129 | 0.4692 | 0.9949   |
| 0.0186        | 3.0   | 5277 | 0.0165          | 0.5980    | 0.5516 | 0.5739 | 0.9957   |
| 0.0154        | 4.0   | 7036 | 0.0143          | 0.5932    | 0.6447 | 0.6179 | 0.9959   |
| 0.0137        | 5.0   | 8795 | 0.0133          | 0.6104    | 0.6581 | 0.6334 | 0.9961   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0a0+b5021ba
- Datasets 2.14.5
- Tokenizers 0.14.1