RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned

ajtamayoh/RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned

RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned is a token classification model from ajtamayoh. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Apr 18, 2024
Updated: Apr 18, 2024
SHA: 7ed0c6c3

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Base models

FacebookAI/xlm-roberta-base

Model card

From the Hugging Face model README

RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2774
Negref Precision: 0.5671
Negref Recall: 0.5899
Negref F1: 0.5782
Neg Precision: 0.9511
Neg Recall: 0.9771
Neg F1: 0.9639
Nsco Precision: 0.8734
Nsco Recall: 0.9181
Nsco F1: 0.8952
Precision: 0.8399
Recall: 0.8727
F1: 0.8560
Accuracy: 0.9615

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: 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: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Negref Precision	Negref Recall	Negref F1	Neg Precision	Neg Recall	Neg F1	Nsco Precision	Nsco Recall	Nsco F1	Precision	Recall	F1	Accuracy
0.1739	1.0	1729	0.1433	0.4553	0.4419	0.4485	0.9216	0.9629	0.9418	0.7944	0.8717	0.8313	0.7799	0.8180	0.7985	0.9555
0.1189	2.0	3458	0.1388	0.4859	0.5476	0.5149	0.9230	0.9683	0.9451	0.7951	0.8895	0.8397	0.7779	0.8494	0.8121	0.9569
0.0944	3.0	5187	0.1374	0.5771	0.5539	0.5653	0.9495	0.9640	0.9567	0.8535	0.8860	0.8695	0.8375	0.8476	0.8425	0.9615
0.0703	4.0	6916	0.1524	0.5415	0.6068	0.5723	0.9410	0.9749	0.9576	0.8476	0.8848	0.8658	0.8164	0.8628	0.8390	0.9602
0.0472	5.0	8645	0.1812	0.5306	0.5687	0.5490	0.9368	0.9716	0.9539	0.8440	0.8931	0.8679	0.8139	0.8566	0.8347	0.9585
0.0371	6.0	10374	0.2231	0.5371	0.5814	0.5584	0.9286	0.9803	0.9538	0.8448	0.9181	0.8799	0.8129	0.8723	0.8415	0.9588
0.031	7.0	12103	0.2036	0.5362	0.5793	0.5569	0.9509	0.9716	0.9611	0.8830	0.9145	0.8985	0.8340	0.8669	0.8501	0.9617
0.0226	8.0	13832	0.2390	0.5392	0.5666	0.5526	0.9507	0.9694	0.9600	0.8751	0.9157	0.8950	0.8335	0.8637	0.8483	0.9589
0.0174	9.0	15561	0.2462	0.5355	0.5899	0.5614	0.9443	0.9803	0.9620	0.8694	0.9169	0.8925	0.8258	0.8736	0.8491	0.9599
0.0098	10.0	17290	0.2751	0.5796	0.5772	0.5784	0.9490	0.9749	0.9618	0.8740	0.9145	0.8938	0.8443	0.8678	0.8559	0.9621
0.008	11.0	19019	0.2760	0.5611	0.5729	0.5669	0.9461	0.9771	0.9613	0.8695	0.9181	0.8931	0.8365	0.8691	0.8525	0.9606
0.0041	12.0	20748	0.2774	0.5671	0.5899	0.5782	0.9511	0.9771	0.9639	0.8734	0.9181	0.8952	0.8399	0.8727	0.8560	0.9615

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Apr 18, 2024
Updated: Apr 18, 2024