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--- |
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library_name: transformers |
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tags: [summarization, transformers, t5, fine-tuning, custom-dataset, text-generation] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is a fine-tuned T5 model for text summarization using the SAMSum dataset. The model has been trained using 🤗 Transformers and Hugging Face Trainer with mixed precision (fp16) to optimize memory efficiency. |
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- **Developed by:** Saravanan K |
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- **Finetuned from model [optional]:** t5-base |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/SARAVANANVIJAY123/DL-Assessment/blob/main/DL-L%26D%20CODE.ipynb |
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### Use Cases |
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### Direct Use |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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This model can be used for text summarization tasks, particularly for summarizing dialogues and conversations. |
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### Downstream Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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The model can be fine-tuned further on other summarization datasets or used in larger NLP applications requiring summarization capabilities. |
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## Out Of Scope Use |
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The model may not perform well on non-dialogue-based text or non-English languages. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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Biases: Since it is trained on the SAMSum dataset, it may have biases related to conversational English data. |
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Limitations: Performance may degrade on texts that are significantly different from the training dataset. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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# Define model name (same as uploaded one) |
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model_name = "Saravanankumaran/summarisation_model" |
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# Load model and tokenizer from Hugging Face Hub |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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print("Model loaded successfully! ✅") |
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# Below is the example text to use the model |
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text = """ |
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Laxmi Kant: what work you planning to give Tom? |
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Juli: i was hoping to send him on a business trip first. |
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Laxmi Kant: cool. is there any suitable work for him? |
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Juli: he did excellent in last quarter. i will assign new project, once he is back. |
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""" |
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inputs = tokenizer(text, return_tensors="pt") |
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output = model.generate(**inputs) |
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summary = tokenizer.decode(output[0], skip_special_tokens=True) |
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print("Generated Output:", summary) |
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