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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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Usage: |
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## Model |
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Base version of e5-v2 finetunned on an annotated subbset of C4 (Numind/C4_sentiment-analysis). This model provide generic embedding for sentiment analysis. |
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## Usage |
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Below is an example to encode text and get embedding. |
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```python |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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model = AutoModel.from_pretrained("Numind/e5-base-SA") |
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tokenizer = AutoTokenizer.from_pretrained("Numind/e5-base-SA") |
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') |
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model.to(device) |
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size = 256 |
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text = "This movie is amazing" |
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encoding = tokenizer( |
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text, |
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truncation=True, |
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padding='max_length', |
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max_length= size, |
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) |
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emb = model( |
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torch.reshape(torch.tensor(encoding.input_ids),(1,len(encoding.input_ids))).to(device),output_hidden_states=True |
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).hidden_states[-1].cpu().detach() |
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embText = torch.mean(emb,axis = 1) |
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``` |
