README.md

metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-finetuned-ner-thesis-dseb
    results: []

xlm-roberta-base-finetuned-ner-thesis-dseb

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1345
• Precision: 0.1786
• Recall: 0.1351
• F1: 0.1538
• Accuracy: 0.9563

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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
0.8175	1.0	12	0.3119	0.0	0.0	0.0	0.9593
0.2019	2.0	24	0.2414	0.0	0.0	0.0	0.9593
0.1156	3.0	36	0.2105	0.0	0.0	0.0	0.9593
0.0913	4.0	48	0.1831	0.0	0.0	0.0	0.9593
0.0987	5.0	60	0.1695	0.0	0.0	0.0	0.9593
0.0697	6.0	72	0.1727	0.0	0.0	0.0	0.9593
0.0528	7.0	84	0.1462	0.0	0.0	0.0	0.9593
0.0538	8.0	96	0.1441	0.0	0.0	0.0	0.9593
0.0504	9.0	108	0.1854	0.0	0.0	0.0	0.9605
0.0359	10.0	120	0.1516	0.0476	0.0312	0.0377	0.9641
0.031	11.0	132	0.1836	0.0	0.0	0.0	0.9621
0.038	12.0	144	0.1581	0.1579	0.0938	0.1176	0.9627
0.0349	13.0	156	0.1901	0.0	0.0	0.0	0.9625
0.0226	14.0	168	0.1740	0.0667	0.0312	0.0426	0.9648
0.0198	15.0	180	0.1729	0.125	0.0625	0.0833	0.9639

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1