Instructions to use Rudra03/xlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rudra03/xlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Rudra03/xlm")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Rudra03/xlm") model = AutoModelForTokenClassification.from_pretrained("Rudra03/xlm") - Notebooks
- Google Colab
- Kaggle
xlm
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: 0.4326
- Precison: 0.8810
- Recall: 0.8753
- F1: 0.8776
- Accuracy: 0.8805
- Jaccard: 0.8125
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precison | Recall | F1 | Accuracy | Jaccard |
|---|---|---|---|---|---|---|---|---|
| 0.3886 | 1.0 | 1513 | 0.2738 | 0.8810 | 0.8753 | 0.8776 | 0.8806 | 0.8126 |
| 0.3168 | 2.0 | 3026 | 0.2826 | 0.8864 | 0.8812 | 0.8833 | 0.8861 | 0.8201 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Rudra03/xlm
Base model
FacebookAI/xlm-roberta-base