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--- |
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license: mit |
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language: |
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- en |
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- ar |
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- fr |
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- de |
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- pt |
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- it |
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- es |
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- zh |
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- ja |
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- ko |
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pipeline_tag: feature-extraction |
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tags: |
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- sentiment-analysis |
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- text-classification |
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- generic |
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- sentiment-classification |
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- multilingual |
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--- |
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## Model |
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Base version of e5-multilingual finetunned on an annotated subset of mC4 (multilingual C4). This model provide generic embedding for sentiment analysis. Embeddings can be used out of the box or fine tune on specific datasets. |
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Blog post: https://www.numind.ai/blog/creating-task-specific-foundation-models-with-gpt-4 |
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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 |
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from transformers import AutoTokenizer, AutoModel |
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model = AutoModel.from_pretrained("Numind/e5-multilingual-sentiment_analysis") |
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tokenizer = AutoTokenizer.from_pretrained("Numind/e5-multilingual-sentiment_analysis") |
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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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``` |
