Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use metz-stdtwist/zoo-intent-results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metz-stdtwist/zoo-intent-results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="metz-stdtwist/zoo-intent-results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("metz-stdtwist/zoo-intent-results") model = AutoModelForSequenceClassification.from_pretrained("metz-stdtwist/zoo-intent-results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
zoo-intent-results
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:
- F1-score: 0.9945
- Loss: 0.0260
- Accuracy: 0.9966
- F1 Macro: 0.9945
- F1 Weighted: 0.9966
- F1 Info: 0.9976
- F1 Routing: 0.9981
- F1 Smalltalk: 0.9861
- F1 Fallback: 0.9963
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | F1-score | Validation Loss | Accuracy | F1 Macro | F1 Weighted | F1 Info | F1 Routing | F1 Smalltalk | F1 Fallback |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0827 | 1.0 | 8000 | 0.9879 | 0.0400 | 0.9927 | 0.9879 | 0.9928 | 0.9945 | 0.9959 | 0.9684 | 0.9928 |
| 0.0211 | 2.0 | 16000 | 0.9914 | 0.0330 | 0.9944 | 0.9914 | 0.9944 | 0.9959 | 0.9961 | 0.9793 | 0.9942 |
| 0.0142 | 3.0 | 24000 | 0.9945 | 0.0260 | 0.9966 | 0.9945 | 0.9966 | 0.9976 | 0.9981 | 0.9861 | 0.9963 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.1.1
- Tokenizers 0.22.1
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Model tree for metz-stdtwist/zoo-intent-results
Base model
FacebookAI/xlm-roberta-base