Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("adriansanz/intent_analysis_xml_5ep_v1")
model = AutoModelForSequenceClassification.from_pretrained("adriansanz/intent_analysis_xml_5ep_v1")Quick Links
intent_analysis
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0440
- Accuracy: 0.9943
- Precision: 0.9943
- Recall: 0.9943
- F1: 0.9943
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2284 | 1.0 | 559 | 0.1132 | 0.9814 | 0.9819 | 0.9815 | 0.9814 |
| 0.085 | 2.0 | 1118 | 0.1069 | 0.9814 | 0.9818 | 0.9814 | 0.9814 |
| 0.0599 | 3.0 | 1677 | 0.0752 | 0.99 | 0.9901 | 0.9900 | 0.9900 |
| 0.0316 | 4.0 | 2236 | 0.0382 | 0.9943 | 0.9943 | 0.9943 | 0.9943 |
| 0.0068 | 5.0 | 2795 | 0.0440 | 0.9943 | 0.9943 | 0.9943 | 0.9943 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for adriansanz/intent_analysis_xml_5ep_v1
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adriansanz/intent_analysis_xml_5ep_v1")