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| import gradio as gr | |
| from transformers import AutoTokenizer | |
| import timm | |
| title = "Finetuning [BERT] on A Financial News Sentiment Dataset" | |
| description = """ | |
| The LLM was finetuned on a Financial News Tweet Sentiment Dataset. The documents have 3 different labels: | |
| "LABEL_0": "Bearish", | |
| "LABEL_1": "Bullish", | |
| "LABEL_2": "Neutral" | |
| <img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px> | |
| """ | |
| article = "Check out the dataset that [BERT cased]((https://huggingface.co/bert-base-cased?text=Paris+is+the+%5BMASK%5D+of+France.)) was [finetuned on](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment/viewer/zeroshot--twitter-financial-news-sentiment/train?row=9505)." | |
| def sentiment_analyzer(tweet): | |
| model_reloaded = timm.create_model('hf_hub:at2507/zeroshot_finetuned_sentiment', pretrained=True) | |
| # model = model.load("models/at2507/zeroshot_finetuned_sentiment") | |
| # gr.Interface.load("models/at2507/zeroshot_finetuned_sentiment").launch() | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") | |
| zeroshotsent_model = pipeline("text-classification", model = model.to('cpu:0'), tokenizer=tokenizer) | |
| return zeroshotsent_model(tweet) | |
| gr.Interface( | |
| fn=sentiment_analyzer, | |
| inputs="textbox", | |
| outputs="text", | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[["CLNE, TRXC, TGE and ADMS among midday movers"], | |
| ["CRISPR Therapeutics among healthcare gainers; Plus Therapeutics leads the losers"]], | |
| ).launch() | |