Text Classification
Transformers
Safetensors
Abkhaz
camembert
fill-mask
code
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("williamC21/camembert-base-test")
model = AutoModelForMaskedLM.from_pretrained("williamC21/camembert-base-test")Quick Links
A newer version of this model is available: Qwen/Qwen3.5-35B-A3B
license: apache-2.0 tags: - text-classification - glue - mrpc datasets: - glue language: - en
bert-finetuned-mrpc-v2
Fine-tuned BERT-base-uncased on MRPC (GLUE benchmark) for paraphrase detection.
Model details
- Base model: google-bert/bert-base-uncased
- Task: Binary classification (paraphrase or not)
- Language: English
- Training data: GLUE MRPC train split
- Evaluation data: GLUE MRPC validation split
- Epochs: 3
- Batch size: 16
- Learning rate: 2e-5
How to use
from transformers import pipeline
classifier = pipeline("text-classification", model="tu-usuario/bert-finetuned-mrpc-v2")
result = classifier("The two sentences mean the same thing.")
print(result)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="williamC21/camembert-base-test")