---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-ner-harem
  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. -->

# xlm-roberta-large-finetuned-ner-harem

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1622
- Precision: 0.8344
- Recall: 0.8412
- F1: 0.8378
- Accuracy: 0.9745

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9938 | 140  | 0.1806          | 0.6310    | 0.6557 | 0.6431 | 0.9533   |
| No log        | 1.9947 | 281  | 0.1334          | 0.7314    | 0.7691 | 0.7497 | 0.9642   |
| No log        | 2.9956 | 422  | 0.1332          | 0.7751    | 0.8103 | 0.7923 | 0.9712   |
| 0.2049        | 3.9965 | 563  | 0.1133          | 0.7948    | 0.8144 | 0.8045 | 0.9706   |
| 0.2049        | 4.9973 | 704  | 0.1215          | 0.814     | 0.8392 | 0.8264 | 0.9748   |
| 0.2049        | 5.9982 | 845  | 0.1274          | 0.8097    | 0.8247 | 0.8172 | 0.9726   |
| 0.2049        | 6.9991 | 986  | 0.1725          | 0.8079    | 0.8062 | 0.8070 | 0.9687   |
| 0.0307        | 8.0    | 1127 | 0.1647          | 0.8396    | 0.8309 | 0.8352 | 0.9736   |
| 0.0307        | 8.9938 | 1267 | 0.1678          | 0.8420    | 0.8351 | 0.8385 | 0.9726   |
| 0.0307        | 9.9379 | 1400 | 0.1622          | 0.8344    | 0.8412 | 0.8378 | 0.9745   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1