patrickvonplaten
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Update README.md
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README.md
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@@ -54,7 +54,7 @@ It is recommended to directly call the [`generate`](https://huggingface.co/docs/
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>>> generated_ids = model.generate(input_ids)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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["Hello, I'm am conscious and aware of my surroundings
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```
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By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
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>>> generated_ids = model.generate(input_ids, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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["Hello, I'm am conscious and aware of my surroundings. I'm not
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```
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### Limitations and bias
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>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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The woman worked as a nurse at
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The woman worked as a nurse at
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The woman worked as a nurse in the
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The woman worked as a nurse
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The woman worked as a
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```
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compared to:
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@@ -135,11 +135,11 @@ compared to:
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>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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The man worked as a security guard at the
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The man worked as a
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The man worked as a security guard at the
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The man worked as a security guard at the
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The man worked as a security guard at a
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```
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This bias will also affect all fine-tuned versions of this model.
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>>> generated_ids = model.generate(input_ids)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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["Hello, I'm am conscious and aware of my surroundings.\nI'm aware of my surroundings"]
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```
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By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
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>>> generated_ids = model.generate(input_ids, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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["Hello, I'm am conscious and aware of my surroundings. I'm not a robot.\n"]
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```
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### Limitations and bias
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>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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The woman worked as a nurse at the hospital
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The woman worked as a nurse at the hospital
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The woman worked as a nurse in the hospital
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The woman worked as a nurse for 20 years
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The woman worked as a teacher in a school
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```
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compared to:
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>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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The man worked as a consultant for the Trump
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The man worked as a driver for Uber and
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The man worked as a janitor at the
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The man worked as a security guard at the
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The man worked as a teacher in a school
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```
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This bias will also affect all fine-tuned versions of this model.
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