---
license: mit
tags:
- generated_from_trainer
datasets:
- favsbot
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-NER-favsbot
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: favsbot
      type: favsbot
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.5555555555555556
    - name: Recall
      type: recall
      value: 0.4722222222222222
    - name: F1
      type: f1
      value: 0.5105105105105106
    - name: Accuracy
      type: accuracy
      value: 0.6900452488687783
---

<!-- 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. -->

# xlm-roberta-base-NER-favsbot

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0572
- Precision: 0.5556
- Recall: 0.4722
- F1: 0.5105
- Accuracy: 0.6900

## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 2.4303          | 0.1448    | 0.3556 | 0.2058 | 0.1855   |
| No log        | 2.0   | 8    | 2.3220          | 0.1465    | 0.3556 | 0.2075 | 0.1991   |
| No log        | 3.0   | 12   | 2.1842          | 0.2486    | 0.2389 | 0.2436 | 0.4593   |
| No log        | 4.0   | 16   | 1.9552          | 0.4       | 0.0111 | 0.0216 | 0.4367   |
| No log        | 5.0   | 20   | 1.6989          | 0.0       | 0.0    | 0.0    | 0.4321   |
| No log        | 6.0   | 24   | 1.6532          | 0.5       | 0.0056 | 0.0110 | 0.4344   |
| No log        | 7.0   | 28   | 1.5724          | 0.3649    | 0.15   | 0.2126 | 0.5045   |
| No log        | 8.0   | 32   | 1.5164          | 0.3654    | 0.2111 | 0.2676 | 0.5271   |
| No log        | 9.0   | 36   | 1.4448          | 0.4203    | 0.1611 | 0.2329 | 0.5090   |
| No log        | 10.0  | 40   | 1.3922          | 0.4833    | 0.1611 | 0.2417 | 0.5158   |
| No log        | 11.0  | 44   | 1.3409          | 0.5395    | 0.2278 | 0.3203 | 0.5498   |
| No log        | 12.0  | 48   | 1.2831          | 0.5824    | 0.2944 | 0.3911 | 0.5950   |
| No log        | 13.0  | 52   | 1.2269          | 0.5714    | 0.3556 | 0.4384 | 0.6335   |
| No log        | 14.0  | 56   | 1.1766          | 0.5625    | 0.4    | 0.4675 | 0.6606   |
| No log        | 15.0  | 60   | 1.1408          | 0.5540    | 0.4278 | 0.4828 | 0.6674   |
| No log        | 16.0  | 64   | 1.1159          | 0.56      | 0.4667 | 0.5091 | 0.6810   |
| No log        | 17.0  | 68   | 1.0908          | 0.5658    | 0.4778 | 0.5181 | 0.6855   |
| No log        | 18.0  | 72   | 1.0722          | 0.5658    | 0.4778 | 0.5181 | 0.6923   |
| No log        | 19.0  | 76   | 1.0615          | 0.5592    | 0.4722 | 0.5120 | 0.6900   |
| No log        | 20.0  | 80   | 1.0572          | 0.5556    | 0.4722 | 0.5105 | 0.6900   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1