stanfordnlp/snli
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How to use IngridCEK/simcse-bert-base-snli-sup with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("IngridCEK/simcse-bert-base-snli-sup")
sentences = [
"That is a happy person",
"That is a happy dog",
"That is a very happy person",
"Today is a sunny day"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]SimCSE (supervised, con hard negatives) sobre bert-base-uncased, entrenado por: Castillo Ingrid, Yeimy Piste.
Subconjunto SNLI de 100k registros (train oficial, solo con consenso de anotadores). Supervisado: pares (premisa, hipótesis de entailment); hard negatives = contradicción de la misma premisa cuando existe (~28%). No se usó MNLI ni otros datos.
pooling cls (sin MLP) · lr 5e-05 · batch 64 · temperatura 0.05 · épocas 3 · dropout 0.1 ·
max_len train 32 / eval 128 · AdamW (wd 0.01) · warmup 0.1 lineal · semilla 42 · mejor checkpoint por Spearman en STS-B dev (paso 200).
| dev | test | alignment (dev) | uniformity (dev) |
|---|---|---|---|
| 82.41 | 78.06 | 0.195 | -3.079 |
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("IngridCEK/simcse-bert-base-snli-sup")
emb = m.encode(["A man plays guitar.", "Someone is making music."], normalize_embeddings=True)
Base model
google-bert/bert-base-uncased