CamemBERT
Collection
Based on Metas's RoBERTa model released in 2019, trained on 138GB of French text. • 1 item • Updated
Text Classification
Transformers
PyTorch
English
camembert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Intel/camembert-base-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("Intel/camembert-base-mrpc")Quick Links
camembert-base-mrpc
This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
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Dataset used to train Intel/camembert-base-mrpc
Collection including Intel/camembert-base-mrpc
Evaluation results
- Accuracy on GLUE MRPCself-reported0.850
- F1 on GLUE MRPCself-reported0.893
- Accuracy on gluevalidation set verified0.850
- Precision on gluevalidation set verified0.876
- Recall on gluevalidation set verified0.910
- AUC on gluevalidation set verified0.903
- F1 on gluevalidation set verified0.893
- loss on gluevalidation set verified0.429
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/camembert-base-mrpc")