File size: 3,597 Bytes
436e51e ac1af04 436e51e c52b190 bfb6817 c52b190 bfb6817 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ontonotesv5-en
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-base-ontonotesv5-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5/viewer/english_v4/train) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1381
- Precision: 0.8637
- Recall: 0.8785
- F1: 0.8710
- Accuracy: 0.9804
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0787 | 1.0 | 2350 | 0.0831 | 0.8119 | 0.8611 | 0.8358 | 0.9765 |
| 0.0565 | 2.0 | 4700 | 0.0756 | 0.8513 | 0.8708 | 0.8609 | 0.9794 |
| 0.0415 | 3.0 | 7050 | 0.0763 | 0.8530 | 0.8739 | 0.8633 | 0.9801 |
| 0.0347 | 4.0 | 9400 | 0.0820 | 0.8558 | 0.8810 | 0.8682 | 0.9804 |
| 0.0252 | 5.0 | 11750 | 0.0913 | 0.8683 | 0.8607 | 0.8645 | 0.9791 |
| 0.0201 | 6.0 | 14100 | 0.0923 | 0.86 | 0.8763 | 0.8681 | 0.9804 |
| 0.0172 | 7.0 | 16450 | 0.1023 | 0.8617 | 0.8788 | 0.8702 | 0.9800 |
| 0.0118 | 8.0 | 18800 | 0.1083 | 0.8579 | 0.8756 | 0.8667 | 0.9799 |
| 0.0101 | 9.0 | 21150 | 0.1162 | 0.8583 | 0.8766 | 0.8674 | 0.9803 |
| 0.009 | 10.0 | 23500 | 0.1189 | 0.8623 | 0.8772 | 0.8697 | 0.9804 |
| 0.0074 | 11.0 | 25850 | 0.1259 | 0.8642 | 0.8757 | 0.8699 | 0.9804 |
| 0.0053 | 12.0 | 28200 | 0.1303 | 0.8601 | 0.8765 | 0.8682 | 0.9800 |
| 0.0046 | 13.0 | 30550 | 0.1345 | 0.8619 | 0.8755 | 0.8686 | 0.9799 |
| 0.004 | 14.0 | 32900 | 0.1381 | 0.8637 | 0.8785 | 0.8710 | 0.9804 |
| 0.0029 | 15.0 | 35250 | 0.1405 | 0.8616 | 0.8788 | 0.8701 | 0.9803 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
## Citation
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |