ValueError

#1
by JanVythikowski - opened

docs_texts = ['My dummy text','My text']
docs_inps = tokenizer(
docs_texts, truncation=True, padding=True, return_tensors="pt"
)

model = AutoModel.from_pretrained('sentence-transformers/gtr-t5-base')
model(**docs_inps)

I get 'ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds'. Nevertheless, the model only uses the encoder.

It seems that you also include the T5 decoder in the architecture.

Sentence Transformers org

Hello!

The recommended usage for this model is via sentence_transformer, e.g.:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/gtr-t5-xl')
embeddings = model.encode(sentences)
print(embeddings)

Otherwise, the Pooling and Dense layers won't be loaded.

tomaarsen changed discussion status to closed

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