Comments (2)
@JohnGiorgi thanks very much for the quick response and resolution.
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Hi @FL33TW00D,
Hmm, that code you copied from the model card does look out of date. Good catch.
I believe that this is what the model card should read, which is similar to what is in this repos README.
import torch
from scipy.spatial.distance import cosine
from transformers import AutoModel, AutoTokenizer
# Load the model
tokenizer = AutoTokenizer.from_pretrained("johngiorgi/declutr-sci-base")
model = AutoModel.from_pretrained("johngiorgi/declutr-sci-base")
# Prepare some text to embed
text = [
"Oncogenic KRAS mutations are common in cancer.",
"Notably, c-Raf has recently been found essential for development of K-Ras-driven NSCLCs.",
]
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
# Embed the text
with torch.no_grad():
sequence_output = model(**inputs)[0]
# Mean pool the token-level embeddings to get sentence-level embeddings
embeddings = torch.sum(
sequence_output * inputs["attention_mask"].unsqueeze(-1), dim=1
) / torch.clamp(torch.sum(inputs["attention_mask"], dim=1, keepdims=True), min=1e-9)
# Compute a semantic similarity via the cosine distance
semantic_sim = 1 - cosine(embeddings[0], embeddings[1])
I checked that this works locally with the latest version of Transformers. I also updated all the model cards to reflect this.
from declutr.
Related Issues (20)
- Saving the model in hugging face format is not working
- Does the training notebook not work in windows jupyter notebook HOT 1
- Cant set up DECLUTR in local AWS linux machine HOT 2
- argument 'lazy' for dataset_reader HOT 2
- Superclass initialization in token embedder HOT 2
- Could not lex the character code 194 HOT 3
- Minimum text length violated despite preprocessing HOT 2
- How to plot the learning curve from the output logs created post training of declutr? HOT 1
- Impact of "shorter" documents (span, number of tokens) for extended pretraining HOT 7
- Installation issue HOT 8
- Wrong training procedure? HOT 6
- Strange issue occuring during Training HOT 2
- load pretrained tf1 model with pytorch HOT 5
- How to integrate a longer sequence model like longformer into declutr architecture HOT 8
- Encoder class breaks for long strings
- can i finetune the model ? HOT 2
- Update DeCLUTR requirements? HOT 5
- How to use a validation dataset when training? HOT 8
- RuntimeError: Error(s) in loading state_dict for DeCLUTR: HOT 2
- Error while encoding HOT 4
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