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The pipeline tag "text-similarity" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other
BiEncoder Regression Model
This model is a BiEncoder architecture that outputs similarity scores between text pairs.
Model Details
- Base Model: bert-base-uncased
- Task: Regression
- Architecture: BiEncoder with cosine similarity
- Loss Function: cosine_embedding
Usage
from transformers import AutoTokenizer, AutoModel
from modeling import BiEncoderModelRegression
# Load model components
tokenizer = AutoTokenizer.from_pretrained("minoosh/bert-reg-biencoder-cosine_embedding")
base_model = AutoModel.from_pretrained("bert-base-uncased")
model = BiEncoderModelRegression(base_model, loss_fn="cosine_embedding")
# Load weights
state_dict = torch.load("pytorch_model.bin")
model.load_state_dict(state_dict)
# Prepare inputs
texts1 = ["first text"]
texts2 = ["second text"]
inputs = tokenizer(
texts1, texts2,
padding=True,
truncation=True,
return_tensors="pt"
)
# Get similarity scores
outputs = model(**inputs)
similarity_scores = outputs["logits"]
Metrics
The model was trained using cosine_embedding loss and evaluated using:
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- Pearson Correlation
- Spearman Correlation
- Cosine Similarity
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