Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`long-t5-tglobal-base`](https://huggingface.co/long-t5-tglobal-base) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.
How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.
**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.
For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).
This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien).
If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!
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# Fine-tuned LongT5 for Conversational QA
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This model is a fine-tuned version of [long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for the task of Conversational QA. The model was fine-tuned on the [SQuADv2](https://huggingface.co/datasets/squad_v2) and [CoQA](https://huggingface.co/datasets/coqa) datasets and on Tryolabs' own custom dataset, [TryoCoQA](https://github.com/tryolabs/TryoCoQA).
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base_model: long-t5-tglobal-base
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# Fine-tuned LongT5 for Conversational QA
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This model is a fine-tuned version of [long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for the task of Conversational QA. The model was fine-tuned on the [SQuADv2](https://huggingface.co/datasets/squad_v2) and [CoQA](https://huggingface.co/datasets/coqa) datasets and on Tryolabs' own custom dataset, [TryoCoQA](https://github.com/tryolabs/TryoCoQA).
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