patrickvonplaten
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Update README.md
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README.md
CHANGED
@@ -55,7 +55,7 @@ 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
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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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@@ -77,7 +77,7 @@ By default, generation is deterministic. In order to use the top-k sampling, ple
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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
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```
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### Limitations and bias
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@@ -110,11 +110,11 @@ Here's an example of how the model can have biased predictions:
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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 at
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The woman worked as a
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```
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compared to:
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@@ -138,9 +138,9 @@ compared to:
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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 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 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 I'm not a robot.\nI'm a robot and"]
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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 I have a question. "]
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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 intensive
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The woman worked as a nurse at the hospital
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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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>>> 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 security guard at the
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The man worked as a teacher in the city
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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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```
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This bias will also affect all fine-tuned versions of this model.
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