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README.md

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+# BART-perspectives Model
+
+## Overview
+
+The BART-perspectives model is a sequence-to-sequence Transformer model hosted on Hugging Face Model Hub. 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.
+
+## Usage
+
+It is designed to be used with the `perspectives` library:
+
+```python
+from perspectives import DataFrame
+
+# Load DataFrame
+df = DataFrame(texts = [list of sentences]) 
+
+# Load model
+df.load_model() 
+
+# Get perspectives
+df.get_perspectives()
+
+# Search
+df.search(speaker='...', emotion='...')
+```
+
+You can use also this model directly with a pipeline for text generation:
+
+```python
+from transformers import pipeline
+
+# Load the model
+generator = pipeline('text-generation', model='helliun/bart-perspectives')
+
+# Get perspective
+perspective = generator("Describe the perspective of this text: <your text>", max_length=1024, do_sample=False)
+print(perspective)
+```
+You can also use it with `transformers.AutoTokenizer` and `transformers.AutoModelForSeq2SeqLM`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model
+tokenizer = AutoTokenizer.from_pretrained("helliun/bart-perspectives")
+model = AutoModelForSeq2SeqLM.from_pretrained("helliun/bart-perspectives")
+
+# Tokenize the sentence
+inputs = tokenizer.encode("Describe the perspective for this sentence: <your text>", return_tensors='pt')
+
+# Pass the tensor through the model
+results = model.generate(inputs)
+
+# Decode the results
+decoded = tokenizer.decode(results[:,0])
+print(decoded)
+```
+
+## Training and Evaluation
+
+The model was fine-tuned on a diverse corpus encompassing multiple subjects and emotion ranges. Please refer to the model's card on Hugging Face for detailed information about specific datasets, metrics, training, and evaluation processes.
+
+## Contributing and Support
+
+Please raise an issue on the [Hugging Face Model card](https://huggingface.co/helliun/bart-perspectives) if you encounter any problems using the model. Contributions like fine-tuning on additional data or improving the model architecture are always welcome!
+
+## License
+
+The model is open source and free to use under the MIT license.