Text Classification
Transformers
PyTorch
Italian
bert
cross-encoder
sentence-similarity
text-embeddings-inference
Instructions to use efederici/cross-encoder-bert-base-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/cross-encoder-bert-base-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="efederici/cross-encoder-bert-base-stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("efederici/cross-encoder-bert-base-stsb") model = AutoModelForSequenceClassification.from_pretrained("efederici/cross-encoder-bert-base-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download CECorrelationEvaluator_sts-dev_results.csv from efederici/cross-encoder-bert-base-stsb: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/efederici/cross-encoder-bert-base-stsb/resolve/main/CECorrelationEvaluator_sts-dev_results.csv
- Command line
-
hf download hf://efederici/cross-encoder-bert-base-stsb/CECorrelationEvaluator_sts-dev_results.csv
-
curl -L -o CECorrelationEvaluator_sts-dev_results.csv https://huggingface.co/efederici/cross-encoder-bert-base-stsb/resolve/main/CECorrelationEvaluator_sts-dev_results.csv
227 Bytes
| epoch,steps,Pearson_Correlation,Spearman_Correlation | |
| 0,-1,0.855315241536417,0.8491118727278618 | |
| 1,-1,0.8676937353054238,0.8639392227916658 | |
| 2,-1,0.8759491508537594,0.871237024968008 | |
| 3,-1,0.874309299933148,0.8691823634113681 | |