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import streamlit as st |
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from transformers import AutoTokenizer, DistilBertForSequenceClassification |
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import torch |
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from torch.nn.functional import softmax |
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@st.cache |
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def load_tags_info(): |
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tag_id = {} |
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id_tag = {} |
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with open('tag.txt', 'r') as file: |
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i = 0 |
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for line in file: |
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tag = line[1:].split(',')[0] |
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tag_id[tag] = i |
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id_tag[i] = tag |
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i += 1 |
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tag_id['None'] = 150 |
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id_tag[150] = 'None' |
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return (tag_id, id_tag) |
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tag_id, id_tag = load_tags_info() |
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@st.cache |
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def load_tokenizer(): |
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return AutoTokenizer.from_pretrained('./') |
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@st.cache |
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def load_model(): |
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return DistilBertForSequenceClassification.from_pretrained('./') |
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def top_xx(preds, xx=95): |
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tops = torch.argsort(preds, 1, descending=True) |
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total = 0 |
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index = 0 |
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result = [] |
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while total < xx / 100: |
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next_id = tops[0, index].item() |
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if next_id == 150: |
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index += 1 |
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continue |
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total += preds[0, next_id] |
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index += 1 |
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result.append(id_tag[next_id]) |
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return result |
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model = load_model() |
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tokenizer = load_tokenizer() |
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title = st.text_area(label='Title', height=30) |
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abstract = st.text_area(label='Abstract (optional)', height=200) |
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st.caption('Generation:') |
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prompt = 'Title: ' + title + ' Abstract: ' + abstract |
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tokens = tokenizer(prompt, truncation=True, padding='max_length', return_tensors='pt')['input_ids'] |
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preds = softmax(model(tokens.reshape(1, -1)).logits , dim=1) |
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tags = top_xx(preds) |
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other_tags = [] |
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st.header('Inferred tags:') |
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for i, tag_data in enumerate(tags): |
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st.caption(tag_data) |
