Merge pull request stanfordnlp#2 from stanfordnlp/main

Merge from main
mgbvox · May 14, 2024 · 1c1e9ac · 1c1e9ac
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diff --git a/README.md b/README.md
@@ -101,7 +101,7 @@ The DSPy documentation is divided into **tutorials** (step-by-step illustration
 
 - [DSPy talk at ScaleByTheBay Nov 2023](https://www.youtube.com/watch?v=Dt3H2ninoeY).
 - [DSPy webinar with MLOps Learners](https://www.youtube.com/watch?v=im7bCLW2aM4), a bit longer with Q&A.
-- Hands-on Overviews of DSPy by the community: [DSPy Explained! by Connor Shorten](https://www.youtube.com/watch?v=41EfOY0Ldkc), [DSPy explained by code_your_own_ai](https://www.youtube.com/watch?v=ycfnKPxBMck), [DSPy Crash Course by AI Bites](https://youtu.be/5-zgASQKkKQ?si=3gnmVouT5_rpk_nu)
+- Hands-on Overviews of DSPy by the community: [DSPy Explained! by Connor Shorten](https://www.youtube.com/watch?v=41EfOY0Ldkc), [DSPy explained by code_your_own_ai](https://www.youtube.com/watch?v=ycfnKPxBMck), [DSPy Crash Course by AI Bites](https://youtu.be/5-zgASQKkKQ?si=3gnmVouT5_rpk_nu), [DSPy Paper Explained by Unify](https://youtu.be/kFB8kFchCH4?si=FuM6L5H5lweanckz)
 - Interviews: [Weaviate Podcast in-person](https://www.youtube.com/watch?v=CDung1LnLbY), and you can find 6-7 other remote podcasts on YouTube from a few different perspectives/audiences.
 - **Tracing in DSPy** with Arize Phoenix: [Tutorial for tracing your prompts and the steps of your DSPy programs](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/dspy_tracing_tutorial.ipynb)
 - [DSPy: Not Your Average Prompt Engineering](https://jina.ai/news/dspy-not-your-average-prompt-engineering), why it's crucial for future prompt engineering, and yet why it is challenging for prompt engineers to learn.

diff --git a/examples/tweets/tweets_assertions.ipynb b/examples/tweets/tweets_assertions.ipynb
@@ -120,7 +120,7 @@
     "\n",
     "Let's introduce a new task: TweetGen. We extend the `Multi-Hop QA` program, but now aim to present the answer generation in the form of a tweet. \n",
     "\n",
-    "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate an tweet that effectively answers the question."
+    "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate a tweet that effectively answers the question."
    ]
   },
   {
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@@ Expand Up / @@ -120,7 +120,7 @@ @@
         "\n",
         "Let's introduce a new task: TweetGen. We extend the `Multi-Hop QA` program, but now aim to present the answer generation in the form of a tweet. \n",
         "\n",
-        "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate an tweet that effectively answers the question."
+        "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate a tweet that effectively answers the question."
        ]
       },
       {
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