Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Zlovoblachko/L1-classifier-Synonyms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zlovoblachko/L1-classifier-Synonyms with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zlovoblachko/L1-classifier-Synonyms")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zlovoblachko/L1-classifier-Synonyms") model = AutoModelForSequenceClassification.from_pretrained("Zlovoblachko/L1-classifier-Synonyms") - Notebooks
- Google Colab
- Kaggle
L1-classifier-Synonyms
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5378
- F1: 0.7309
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 73 | 0.6306 | 0.4124 |
| No log | 2.0 | 146 | 0.5541 | 0.6410 |
| No log | 3.0 | 219 | 0.5912 | 0.6897 |
| No log | 4.0 | 292 | 0.5261 | 0.7244 |
| No log | 5.0 | 365 | 0.5378 | 0.7309 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Zlovoblachko/L1-classifier-Synonyms
Base model
FacebookAI/xlm-roberta-base