---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLMRoberta-base-amazon-massive-NER
  results: []
widget:
- text: Maria has an exam at five am this week
datasets:
- AmazonScience/massive
language:
- en
- ru
---

<!-- 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. -->

# XLMRoberta-base-amazon-massive-NER

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the MASSIVE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2907
- Precision: 0.6189
- Recall: 0.6243
- F1: 0.6123
- Accuracy: 0.9200

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9645        | 1.0   | 720  | 0.4148          | 0.4631    | 0.4177 | 0.4154 | 0.8950   |
| 0.4421        | 2.0   | 1440 | 0.3181          | 0.5808    | 0.6001 | 0.5780 | 0.9154   |
| 0.2514        | 3.0   | 2160 | 0.2907          | 0.6189    | 0.6243 | 0.6123 | 0.9200   |
| 0.2117        | 4.0   | 2880 | 0.2967          | 0.6522    | 0.6351 | 0.6352 | 0.9252   |
| 0.1592        | 5.0   | 3600 | 0.3090          | 0.6288    | 0.6923 | 0.6520 | 0.9233   |
| 0.131         | 6.0   | 4320 | 0.2961          | 0.6619    | 0.6693 | 0.6546 | 0.9282   |
| 0.1054        | 7.0   | 5040 | 0.3147          | 0.6424    | 0.6762 | 0.6498 | 0.9260   |
| 0.0923        | 8.0   | 5760 | 0.3171          | 0.6447    | 0.6945 | 0.6614 | 0.9257   |
| 0.0845        | 9.0   | 6480 | 0.3328          | 0.6434    | 0.6791 | 0.6539 | 0.9256   |
| 0.0691        | 10.0  | 7200 | 0.3314          | 0.6628    | 0.6834 | 0.6635 | 0.9264   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1