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--- |
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language: en |
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tags: |
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- bert |
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- sentiment-analysis |
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- text-classification |
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license: mit |
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pipeline_tag: text-classification |
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library_name: transformers |
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widget: |
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- text: I had surgery last month. and I was very impressed with the quality of service |
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from the moment I got in till I left. Also I like to mention the nurses they were |
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out standing |
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example_title: Positive |
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- text: I received the update and will review it later this week. |
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example_title: Neutral |
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- text: Dirty. Generally poor attitude among the nurses, even the good know the place |
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sucks. When patients are crying for help nurse should not be busy watching Tik-Tok. |
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Too many mistakes made too often. Teaching nurses instructing student nurse procedures |
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incorrectly. Yes, it is bad. |
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example_title: Negative |
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base_model: |
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- google/bert_uncased_L-2_H-128_A-2 |
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--- |
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# Bert_sentiment_classifier |
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A BERT (`bert-base-uncased`) model fine-tuned for **3-class sentiment classification**: |
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- **Positive** |
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- **Neutral** |
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- **Negative** |
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## Labels |
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| id | label | |
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|---:|----------| |
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| 0 | Neutral | |
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| 1 | Positive | |
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| 2 | Negative | |
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## Test Drive |
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Try one of these examples into the widget: |
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- **Negative:** "Dirty. Generally poor attitude among the nurses, even the good know the place sucks. When patients are crying for help nurse should not be busy watching Tik-Tok. Too many mistakes made too often. Teaching nurses instructing student nurse procedures incorrectly. Yes, it is bad." |
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- **Neutral:** "I received the update and will review it later this week." |
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- **Positive:** "I had surgery last month. and I was very impressed with the quality of service from the moment I got in till I left. Also I like to mention the nurses they were out standing" |
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## Try it out |
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<div style="position:relative; width:100%; height:0; padding-bottom:75%;"> |
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<iframe |
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src="https://huggingface.co/spaces/pokwir/bert-sentiment-demo" |
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frameborder="0" |
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allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; clipboard-read; clipboard-write; display-capture; encrypted-media; fullscreen; geolocation; gyroscope; hid; identity-credentials-get; idle-detection; local-fonts; magnetometer; microphone; midi; payment; picture-in-picture; publickey-credentials-get; screen-wake-lock; serial; usb; web-share; xr-spatial-tracking" |
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style="position:absolute; top:0; left:0; width:100%; height:100%; border:0; border-radius:12px;" |
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></iframe> |
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</div> |
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## How to use |
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### Transformers pipeline |
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```python |
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from transformers import pipeline |
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clf = pipeline( |
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"text-classification", |
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model="pokwir/Bert_sentiment_classifier", |
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tokenizer="pokwir/Bert_sentiment_classifier", |
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return_all_scores=True |
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) |
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texts = [ |
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"Dirty. Generally poor attitude among the nurses, even the good know the place sucks. When patients are crying for help nurse should not be busy watching Tik-Tok. Too many mistakes made too often. Teaching nurses instructing student nurse procedures incorrectly. Yes, it is bad.", |
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"I had surgery last month. and I was very impressed with the quality of service from the moment I got in till I left. Also I like to mention the nurses they were out standing.", |
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"This hospital has been going downhill for years thanks to dr.billie and her know all attitude she should go back to her vet clinic." |
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] |
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print(clf(texts)) |
