Update README.md

Author: okhat

README.md

@@ -76,7 +76,7 @@ The DSPy documentation is divided into **tutorials** (step-by-step illustration
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 | Beginner |  [**Getting Started**](intro.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)  |  Introduces the basic building blocks in DSPy. Tackles the task of complex question answering with HotPotQA. |
 | Beginner | [**Compiling for Tricky Tasks**](examples/nli/scone/scone.ipynb) | N/A | Teaches LMs to reason about logical statements and negation. Uses GPT-4 to bootstrap few-shot CoT demonstations for GPT-3.5. Establishes a state-of-the-art result on [ScoNe](https://arxiv.org/abs/2305.19426). Contributed by [Chris Potts](https://twitter.com/ChrisGPotts/status/1740033519446057077). |
-| Beginner | [**Local Models & Custom Training Data**](skycamp2023.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/skycamp2023.ipynb) | Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development.
+| Beginner | [**Local Models & Custom Datasets**](skycamp2023.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/skycamp2023.ipynb) | Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development.
 | Intermediate | [**The DSPy Paper**](https://arxiv.org/abs/2310.03714) | N/A | Sections 3, 5, 6, and 7 of the DSPy paper can be consumed as a tutorial. They include explained code snippets, results, and discussions of the abstractions and API.
 | Intermediate | [**Finetuning for Complex Programs**](https://twitter.com/lateinteraction/status/1712135660797317577) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/skycamp2023.ipynb) | Teaches a local T5 model (770M) to do exceptionally well on HotPotQA. Uses only 200 labeled answers. Uses no hand-written prompts, no calls to OpenAI, and no labels for retrieval or reasoning.
 | Advanced | [**Information Extraction**](https://twitter.com/KarelDoostrlnck/status/1724991014207930696) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/drive/1CpsOiLiLYKeGrhmq579_FmtGsD5uZ3Qe) | Tackles extracting information from long articles (biomedical research papers). Combines in-context learning and retrieval to set SOTA on BioDEX. Contributed by [Karel D’Oosterlinck](https://twitter.com/KarelDoostrlnck/status/1724991014207930696).  |