Instructions to use Saed2023/LILT-finetuned-cord_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saed2023/LILT-finetuned-cord_100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Saed2023/LILT-finetuned-cord_100")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Saed2023/LILT-finetuned-cord_100") model = AutoModelForTokenClassification.from_pretrained("Saed2023/LILT-finetuned-cord_100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
LILT-finetuned-cord_100
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4893
- Precision: 0.9070
- Recall: 0.8864
- F1: 0.8966
- Accuracy: 0.9365
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 3.33 | 50 | 0.7078 | 0.8372 | 0.8182 | 0.8276 | 0.8889 |
| No log | 6.67 | 100 | 0.4893 | 0.9070 | 0.8864 | 0.8966 | 0.9365 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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