Token Classification
Transformers
PyTorch
roberta
LABEL-0 = NONE
LABEL-1 = B-DATE
LABEL-2 = I-DATE
LABEL-3 = B-TIME
LABEL-4 = I-TIME
LABEL-5 = B-DURATION
LABEL-6 = I-DURATION
LABEL-7 = B-SET
LABEL-8 = I-SET
Instructions to use asdc/Bio-RoBERTime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asdc/Bio-RoBERTime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="asdc/Bio-RoBERTime")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asdc/Bio-RoBERTime") model = AutoModelForTokenClassification.from_pretrained("asdc/Bio-RoBERTime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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- lr_scheduler_type: linear
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- num_epochs: 24
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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| 0.0433 | 1.0 | 12 | 0.0443 | 0.4948 | 0.5 | 0.4974 | 0.9800 |
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| 0.0234 | 2.0 | 24 | 0.0221 | 0.4082 | 0.7257 | 0.5225 | 0.9732 |
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| 0.0055 | 3.0 | 36 | 0.0159 | 0.4768 | 0.7847 | 0.5932 | 0.9797 |
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| 0.0089 | 4.0 | 48 | 0.0153 | 0.5317 | 0.8160 | 0.6438 | 0.9813 |
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| 0.0033 | 5.0 | 60 | 0.0131 | 0.7229 | 0.8333 | 0.7742 | 0.9896 |
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| 0.008 | 6.0 | 72 | 0.0129 | 0.6649 | 0.8681 | 0.7530 | 0.9885 |
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| 0.0063 | 7.0 | 84 | 0.0146 | 0.7523 | 0.8542 | 0.8 | 0.9904 |
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| 0.0086 | 8.0 | 96 | 0.0150 | 0.7470 | 0.8715 | 0.8045 | 0.9906 |
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| 0.0009 | 9.0 | 108 | 0.0139 | 0.7658 | 0.8854 | 0.8213 | 0.9910 |
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| 0.0031 | 10.0 | 120 | 0.0159 | 0.8031 | 0.8924 | 0.8454 | 0.9919 |
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| 0.0011 | 11.0 | 132 | 0.0158 | 0.7649 | 0.8924 | 0.8237 | 0.9909 |
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| 0.0006 | 12.0 | 144 | 0.0153 | 0.7398 | 0.8785 | 0.8032 | 0.9902 |
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| 0.0013 | 13.0 | 156 | 0.0157 | 0.7815 | 0.8819 | 0.8287 | 0.9910 |
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| 0.0008 | 14.0 | 168 | 0.0154 | 0.7822 | 0.8854 | 0.8306 | 0.9908 |
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| 0.0008 | 15.0 | 180 | 0.0164 | 0.7778 | 0.875 | 0.8235 | 0.9910 |
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| 0.0007 | 16.0 | 192 | 0.0168 | 0.7864 | 0.8819 | 0.8314 | 0.9912 |
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| 0.0018 | 17.0 | 204 | 0.0173 | 0.7870 | 0.8854 | 0.8333 | 0.9912 |
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| 0.0006 | 18.0 | 216 | 0.0178 | 0.7730 | 0.875 | 0.8208 | 0.9914 |
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| 0.0012 | 19.0 | 228 | 0.0171 | 0.8013 | 0.8819 | 0.8397 | 0.9916 |
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| 0.0006 | 20.0 | 240 | 0.0181 | 0.8137 | 0.8646 | 0.8384 | 0.9916 |
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| 0.0007 | 21.0 | 252 | 0.0186 | 0.8137 | 0.8646 | 0.8384 | 0.9918 |
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| 0.0012 | 22.0 | 264 | 0.0188 | 0.8137 | 0.8646 | 0.8384 | 0.9919 |
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| 0.0006 | 23.0 | 276 | 0.0178 | 0.8121 | 0.8854 | 0.8472 | 0.9919 |
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| 0.0009 | 24.0 | 288 | 0.0177 | 0.8121 | 0.8854 | 0.8472 | 0.9919 |
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### Framework versions
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- Transformers 4.24.0
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## Model description
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**Developed by**: Alejandro Sánchez de Castro, Juan Martínez Romo, Lourdes Araujo
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This model is the result of the paper "RoBERTime: A novel model for the detection of temporal expressions in Spanish"
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**Cite as**:
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@article{sanchez2023robertime,
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title={RoBERTime: A novel model for the detection of temporal expressions in Spanish},
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author={S{\'a}nchez-de-Castro-Fern{\'a}ndez, Alejandro and Araujo Serna, Lourdes and Mart{\'\i}nez Romo, Juan},
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year={2023},
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publisher={Sociedad Espa{\~n}ola para el Procesamiento del Lenguaje Natural}
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}
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## Intended uses & limitations
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This model is prepared for the detection of temporal expressions extension in Spanish. It may work in other languages due to RoBERTa multilingual capabilities. This model does not normalize the expression value. This is considered to be a separate task.
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## Training and evaluation data
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- lr_scheduler_type: linear
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- num_epochs: 24
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### Framework versions
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- Transformers 4.24.0
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