Token Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use AICODER009/xlm-roberta-base-finetuned-panx-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AICODER009/xlm-roberta-base-finetuned-panx-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AICODER009/xlm-roberta-base-finetuned-panx-fr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AICODER009/xlm-roberta-base-finetuned-panx-fr") model = AutoModelForTokenClassification.from_pretrained("AICODER009/xlm-roberta-base-finetuned-panx-fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AICODER009/xlm-roberta-base-finetuned-panx-fr: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/AICODER009/xlm-roberta-base-finetuned-panx-fr/resolve/main/README.md
- Command line
-
hf download hf://AICODER009/xlm-roberta-base-finetuned-panx-fr/README.md
-
curl -L -o README.md https://huggingface.co/AICODER009/xlm-roberta-base-finetuned-panx-fr/resolve/main/README.md
1.77 kB
metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: validation
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8446995273463875
xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set:
- Loss: 0.2731
- F1: 0.8447
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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5823 | 1.0 | 191 | 0.3000 | 0.7951 |
| 0.264 | 2.0 | 382 | 0.2782 | 0.8208 |
| 0.177 | 3.0 | 573 | 0.2731 | 0.8447 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0