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--- |
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license: mit |
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datasets: |
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- mteb/tweet_sentiment_extraction |
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language: |
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- en |
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library_name: transformers |
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--- |
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# bart-perspectives |
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## Overview |
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The BART-perspectives model is a sequence-to-sequence transformers mode;. Built on top of Facebook's BART-large (specifically the `philschmid/bart-large-cnn-samsum` finetune), it is specifically designed to extract perspectives from textual data at scale. The model provides an in-depth analysis of the speaker's identity, their emotions, the object of these emotions, and the reason behind these emotions. |
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## Usage |
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It is designed to be used with the `perspectives` library: |
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```python |
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from perspectives import DataFrame |
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# Load DataFrame |
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df = DataFrame(texts = [list of sentences]) |
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# Get perspectives |
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df.get_perspectives() |
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# Search |
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df.search(speaker='...', emotion='...') |
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``` |
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You can use also this model directly with a pipeline for text generation: |
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```python |
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from transformers import pipeline |
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# Load the model |
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generator = pipeline('text-generation', model='helliun/bart-perspectives') |
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# Get perspective |
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perspective = generator("Describe the perspective of this text: <your text>", max_length=1024, do_sample=False) |
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print(perspective) |
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``` |
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You can also use it with `transformers.AutoTokenizer` and `transformers.AutoModelForSeq2SeqLM`: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load the model |
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tokenizer = AutoTokenizer.from_pretrained("helliun/bart-perspectives") |
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model = AutoModelForSeq2SeqLM.from_pretrained("helliun/bart-perspectives") |
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# Tokenize the sentence |
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inputs = tokenizer.encode("Describe the perspective for this sentence: <your text>", return_tensors='pt') |
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# Pass the tensor through the model |
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results = model.generate(inputs) |
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# Decode the results |
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decoded = tokenizer.decode(results[:,0]) |
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print(decoded) |
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``` |
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## Training |
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The model was fine-tuned on a subset of the `mteb/tweet-sentiment-extraction` dataset with emotional analyses generated synthetically by GPT-4. |
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## About me |
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I'm a recent grad of Ohio State University where I did an undergraduate thesis on Synthetic Data Augmentation using LLMs. I've worked as an NLP consultant for a couple awesome startups, and now I'm looking for a role with an inspiring company who is as interested in the untapped potential of LMs as I am! [Here's my LinkedIn.](https://www.linkedin.com/in/henry-leonardi-a63851165/) |
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## Contributing and Support |
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Please raise an issue here if you encounter any problems using the model. Contributions like fine-tuning on additional data or improving the model architecture are always welcome! |
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[Buy me a coffee!](https://www.buymeacoffee.com/helliun) |
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## License |
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The model is open source and free to use under the MIT license. |
