Instructions to use rpharale/dialog_summarization_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rpharale/dialog_summarization_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="rpharale/dialog_summarization_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rpharale/dialog_summarization_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rpharale/dialog_summarization_model: direct link, hf CLI and curl.
- Browser
- Download file 2.31 kB
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https://huggingface.co/rpharale/dialog_summarization_model/resolve/main/README.md
- Command line
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hf download hf://rpharale/dialog_summarization_model/README.md
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curl -L -o README.md https://huggingface.co/rpharale/dialog_summarization_model/resolve/main/README.md
2.31 kB
| license: apache-2.0 | |
| datasets: | |
| - Samsung/samsum | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: summarization | |
| # Dialog summarization model | |
| The `llama_3.1_8b_dialog_summarization_model` model is a dialog summarization model fine-tuned on the `llama-3.1-8b` architecture using LORA. This powerful model excels at generating abstractive summaries from input text. It has been fine-tuned on samsum dataset. | |
| ## Intended Uses & Limitations | |
| ### Intended Uses | |
| - Dialog summarization: The model is well-suited for summarizing lengthy dialogs, making it valuable for tasks that needs to capture lengthy dialog in few lines. | |
| ### Limitations | |
| - Domain-specific content: While it performs well on general text summarization tasks, its performance may vary when applied to domain-specific content. | |
| ## Training and Evaluation Data | |
| The model has been trained on the Samsum dataset. These datasets provide a wide range of dialog summarization examples. | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - num_epochs: 1 | |
| - model_name = "meta-llama/Meta-Llama-3.1-8B" | |
| - num_epochs = 1 | |
| - gradient_accumulation_steps = 4 | |
| - batch_size_training = 1 | |
| - lr = 3e-4 | |
| - use_fp16 = True | |
| - context_length = 1024 | |
| ### Example results | |
| Summarize this dialog: | |
| A: Hi Tom, are you busy tomorrow’s afternoon? | |
| B: I’m pretty sure I am. What’s up? | |
| A: Can you go with me to the animal shelter?. | |
| B: What do you want to do? | |
| A: I want to get a puppy for my son. | |
| B: That will make him so happy. | |
| A: Yeah, we’ve discussed it many times. I think he’s ready now. | |
| B: That’s good. Raising a dog is a tough issue. Like having a baby ;-) | |
| A: I'll get him one of those little dogs. | |
| B: One that won't grow up too big;-) | |
| A: And eat too much;-)) | |
| B: Do you know which one he would like? | |
| A: Oh, yes, I took him there last Monday. He showed me one that he really liked. | |
| B: I bet you had to drag him away. | |
| A: He wanted to take it home right away ;-). | |
| B: I wonder what he'll name it. | |
| A: He said he’d name it after his dead hamster – Lemmy - he's a great Motorhead fan :-))) | |
| Summary: | |
| A wants to get a puppy for her son. A took him to the animal shelter last Monday and he showed her a dog he liked. A wants to get a small dog. A son wants to name it Lemmy. |