File size: 2,500 Bytes
c0de02a a0be2a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import gradio as gr
import model_wrapper
model = model_wrapper.PredictionModel()
def pretty_print_opinion(opinion_dict):
res = []
maxlen = max([len(key) for key in opinion_dict.keys()]) + 2
maxlen = 0
for key, value in opinion_dict.items():
if key == 'Polarity':
res.append(f'{(key + ":").ljust(maxlen)} {value}')
else:
res.append(f'{(key + ":").ljust(maxlen)} \'{" ".join(value[0])}\'')
return '\n'.join(res) + '\n'
def predict(text):
predictions = model.predict([text])
prediction = predictions[0]
results = []
if not prediction['opinions']:
return 'No opinions detected'
for opinion in prediction['opinions']:
results.append(pretty_print_opinion(opinion))
return '\n'.join(results)
markdown_text = '''
<br>
<br>
This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec).
This model is an implementation of the paper "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph).
The current model uses the 'labeled-edge' graph encoding, and achieves the following results on the NoReC dataset:
| Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision |
|:----------------------------:|:----------:|:---------------------------:|
| 0.393 | 0.468 | 0.939 |
The model can be easily used for predicting sentiment tuples as follows:
```python
>>> import model_wrapper
>>> model = model_wrapper.PredictionModel()
>>> model.predict(['vi liker svart kaffe'])
[{'sent_id': '0',
'text': 'vi liker svart kaffe',
'opinions': [{'Source': [['vi'], ['0:2']],
'Target': [['svart', 'kaffe'], ['9:14', '15:20']],
'Polar_expression': [['liker'], ['3:8']],
'Polarity': 'Positive'}]}]
```
'''
with gr.Blocks() as demo:
with gr.Row(equal_height=False) as row:
text_input = gr.Textbox(label="input")
text_output = gr.Textbox(label="output")
with gr.Row(scale=4) as row:
text_button = gr.Button("submit").style(full_width=True)
text_button.click(fn=predict, inputs=text_input, outputs=text_output)
gr.Markdown(markdown_text)
demo.launch()
|