Hey,

Thank you for using this model! Could you please provide a code snippet, so I can know what you are trying to do?

Sure changed config to "output_hidden_states:true"

from transformers import (
TokenClassificationPipeline,
AutoModelForTokenClassification,
AutoTokenizer,
)

Define keyphrase extraction pipeline

class KeyphraseExtractionPipeline(TokenClassificationPipeline):
def init(self, model, *args, **kwargs):
super().init(
model=AutoModelForTokenClassification.from_pretrained(model_path),
tokenizer=AutoTokenizer.from_pretrained(model),
*args,
**kwargs
)

def _forward(self, model_inputs):
    # Forward
    special_tokens_mask = model_inputs.pop("special_tokens_mask")
    offset_mapping = model_inputs.pop("offset_mapping", None)
    sentence = model_inputs.pop("sentence")
    outputs = self.model(**model_inputs)
    logits = outputs[0]

    # I am talking abouth these outputs, they now contain ["hidden_state"]
    embedding = get_embeddings(outputs[1])

    return {
        "logits": logits,
        "special_tokens_mask": special_tokens_mask,
        "offset_mapping": offset_mapping,
        "sentence": sentence,
        "hidden_state": outputs[1],
        "embedding": embedding,
        **model_inputs,
    }


def postprocess(self, model_outputs):
    results = super().postprocess(
        model_outputs=model_outputs,
        aggregation_strategy=AggregationStrategy.SIMPLE,
    )
    return {**model_outputs, **{"keywords": np.unique([result.get("word").strip() for result in results]).tolist()}}

model_path = "keyphrase-extraction-kbir-inspec"
extractor = KeyphraseExtractionPipeline(model=model_path)

Hey @Janni ,

There is a reason why there are 25 layers. The output of the embeddings is also included. So the hidden layers exist out of the output of each layer + the output of the embeddings.

You can find more information in this GitHub thread: https://github.com/huggingface/transformers/issues/1332.
You can also find this in the HuggingFace Roberta documentation.

Hope it helps!

Kind regards,
Thomas De Decker