Fix some typos. (#17560)
* Fix some typos. Signed-off-by: Yulv-git <yulvchi@qq.com> * Fix typo. Signed-off-by: Yulv-git <yulvchi@qq.com> * make fixup.
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@@ -49,7 +49,7 @@ At the end of the community week, each team should submit a demo of their projec
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- **23.06.** Official announcement of the community week. Make sure to sign-up in [this google form](https://forms.gle/tVGPhjKXyEsSgUcs8).
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- **23.06. - 30.06.** Participants will be added to an internal Slack channel. Project ideas can be proposed here and groups of 3-5 are formed. Read this document for more information.
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- **30.06.** Release of all relevant training scripts in JAX/Flax as well as other documents on how to set up a TPU, how to use the training scripts, how to submit a demo, tips & tricks for JAX/Flax, tips & tricks for efficient use of the hub.
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- **30.06.** Release of all relevant training scripts in JAX/Flax as well as other documents on how to set up a TPU, how to use the training scripts, how to submit a demo, tips & tricks for JAX/Flax, tips & tricks for efficient use of the hub.
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- **30.06. - 2.07.** Talks about JAX/Flax, TPU, Transformers, Computer Vision & NLP will be held.
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- **7.07.** Start of the community week! Access to TPUv3-8 will be given to each team.
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- **7.07. - 14.07.** The Hugging Face & JAX/Flax & Cloud team will be available for any questions, problems the teams might run into.
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@@ -106,7 +106,7 @@ def main():
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return start_logits, end_logits, jnp.argmax(pooled_logits, axis=-1)
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def evaluate(example):
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# encode question and context so that they are seperated by a tokenizer.sep_token and cut at max_length
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# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
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inputs = tokenizer(
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example["question"],
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example["context"],
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@@ -22,7 +22,7 @@ the JAX/Flax backend and the [`pjit`](https://jax.readthedocs.io/en/latest/jax.e
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> Note: The example is experimental and might have bugs. Also currently it only supports single V3-8.
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The `partition.py` file defines the `PyTree` of `ParitionSpec` for the GPTNeo model which describes how the model will be sharded.
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The actual sharding is auto-matically handled by `pjit`. The weights are sharded accross all local devices.
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The actual sharding is auto-matically handled by `pjit`. The weights are sharded across all local devices.
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To adapt the script for other models, we need to also change the `ParitionSpec` accordingly.
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TODO: Add more explantion.
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