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* added GPTNeoForTokenClassification * add to top-level init * fixup * test * more fixup * add to gpt_neo.mdx * repo consistency * dummy copy * fix copies * optax >= 0.1.5 assumes jax.Array exists - which it doesn't for jax <= 0.3.6 * merge with main made this superfluous * added classifier_dropout * remove legacy code * removed fmt:on/off removed expected_outputs * doc style fix * classifier_dropout is always in config --------- Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
91 lines
2.5 KiB
Plaintext
91 lines
2.5 KiB
Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# GPT Neo
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## Overview
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The GPTNeo model was released in the [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) repository by Sid
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Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
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[Pile](https://pile.eleuther.ai/) dataset.
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The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
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256 tokens.
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This model was contributed by [valhalla](https://huggingface.co/valhalla).
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### Generation
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The `generate()` method can be used to generate text using GPT Neo model.
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```python
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>>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer
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>>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
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>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
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>>> prompt = (
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... "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
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... "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
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... "researchers was the fact that the unicorns spoke perfect English."
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... )
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>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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>>> gen_tokens = model.generate(
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... input_ids,
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... do_sample=True,
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... temperature=0.9,
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... max_length=100,
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... )
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>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
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```
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## Documentation resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Causal language modeling task guide](../tasks/language_modeling)
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## GPTNeoConfig
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[[autodoc]] GPTNeoConfig
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## GPTNeoModel
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[[autodoc]] GPTNeoModel
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- forward
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## GPTNeoForCausalLM
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[[autodoc]] GPTNeoForCausalLM
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- forward
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## GPTNeoForSequenceClassification
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[[autodoc]] GPTNeoForSequenceClassification
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- forward
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## GPTNeoForTokenClassification
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[[autodoc]] GPTNeoForTokenClassification
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- forward
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## FlaxGPTNeoModel
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[[autodoc]] FlaxGPTNeoModel
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- __call__
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## FlaxGPTNeoForCausalLM
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[[autodoc]] FlaxGPTNeoForCausalLM
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- __call__
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