ShauryaNova/autotrain-tuac9-vfsuc

Model Trained Using AutoTrain

• Problem type: Sentence Transformers

Validation Metrics

loss: 9.164422988891602

validation_pearson_cosine: -0.10073561135203735

validation_spearman_cosine: -0.05129891760425771

validation_pearson_manhattan: -0.07223520049199797

validation_spearman_manhattan: -0.05129891760425771

validation_pearson_euclidean: -0.056592337170460805

validation_spearman_euclidean: -0.05129891760425771

validation_pearson_dot: -0.1007351930231386

validation_spearman_dot: -0.05129891760425771

validation_pearson_max: -0.056592337170460805

validation_spearman_max: -0.05129891760425771

runtime: 0.1267

samples_per_second: 39.454

steps_per_second: 7.891

: 3.0

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the Hugging Face Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: autotrain',
    'search_query: auto train',
    'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)