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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | the repetition of n-grams present in the prompt.
It was designed to promote chattiness in a language model, by preventing the generation of n-grams present in
previous conversation rounds.
Args:
encoder_ngram_size (`int`):
All ngrams of size `ngram_size` can only occur within the encoder input ids.
encoder_input_id... | 427_7_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer("Alice: I love cats. What do you love?\nBob:", return_tensors="pt") | 427_7_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> inputs = tokenizer("Alice: I love cats. What do you love?\nBob:", return_tensors="pt")
>>> # With greedy decoding, we see Bob repeating Alice's opinion. If Bob was a chatbot, it would be a poor one.
>>> outputs = model.generate(**inputs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice... | 427_7_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With this logits processor, we can prevent Bob from repeating Alice's opinion.
>>> outputs = model.generate(**inputs, encoder_no_repeat_ngram_size=2)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice: I love cats. What do you love?
Bob: My cats are very cute.
```
- __call__
[`Logit... | 427_7_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | - __call__
[`LogitsProcessor`] that works similarly to [`RepetitionPenaltyLogitsProcessor`], but with an *inverse* penalty
that is applied to the tokens present in the prompt. In other words, a penalty above 1.0 increases the odds of
selecting tokens that were present in the prompt.
It was designed to avoid halluci... | 427_7_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | for encoder-decoder models, it can also be used with decoder-only models like LLMs.
Args:
penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 rewards prompt tokens. Between 0.0
and 1.0 penalizes prompt tokens.
encoder_input_ids (`torch.LongTensor`):
The encoder_input_ids that shou... | 427_7_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer(["Alice and Bob. The third member's name was"], return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch_decode(gen_out,... | 427_7_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With the `encoder_repetition_penalty` argument we can trigger this logits processor in `generate`, which can
>>> # promote the use of prompt tokens ("Bob" in this example)
>>> gen_out = model.generate(**inputs, encoder_repetition_penalty=1.2)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])... | 427_7_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | Alice and Bob. The third member's name was Bob. The third member's name was Bob.
```
- __call__
[`LogitsProcessor`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Languag... | 427_7_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
Args:
epsilon (`float`):
If set to > 0, only the most tokens with probabilities `epsilon` or higher are kept for generation.
filter_value (`float`, *optional*, defaults to -inf):
All filtered values will be set to this float value.
min_tokens_to_kee... | 427_7_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> set_seed(1)
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = mode... | 427_7_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With epsilon sampling, the output gets restricted to high-probability tokens. Note that this is similar to
>>> # Top P sampling, which restricts tokens based on their cumulative probability.
>>> # Pro tip: The paper recomends using `epsilon_cutoff` values between 3e-4 and 9e-4
>>> outputs = model.generate(**input... | 427_7_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | A sequence: 1, 2, 3, 4, 5, 6, 7, 8, 9
```
- __call__
[`LogitsProcessor`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
cutoff value, `eta`, which is calculated based on a combination of the hyperparameter `epsilon` and the entropy of
the token probabilities, i.e. `e... | 427_7_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | the token probabilities, i.e. `eta := min(epsilon, sqrt(epsilon * e^-entropy(probabilities)))`. Takes the largest
min_tokens_to_keep tokens if no tokens satisfy this constraint. It addresses the issue of poor quality in long
samples of text generated by neural language models leading to more coherent and fluent text. S... | 427_7_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | must be set to `True` for this `LogitsProcessor` to work.
Args:
epsilon (`float`):
A float value in the range (0, 1). Hyperparameter used to calculate the dynamic cutoff value, `eta`. The
suggested values from the paper ranges from 3e-4 to 4e-3 depending on the size of the model.
filter_value (`float`, *optional*, de... | 427_7_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | All values that are found to be below the dynamic cutoff value, `eta`, are set to this float value. This
parameter is useful when logits need to be modified for very low probability tokens that should be excluded
from generation entirely.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Specifies the minimum numb... | 427_7_22 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | For example, if `min_tokens_to_keep` is set to 1, at least one token will always be kept for generation,
even if all tokens have probabilities below the cutoff `eta`.
device (`str`, *optional*, defaults to `"cpu"`):
The device to allocate the tensors.
Examples:
```python
>>> from transformers import AutoTokenizer, Au... | 427_7_23 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> set_seed(1)
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = mode... | 427_7_24 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With eta sampling, the output gets restricted to high-probability tokens. You can see it as a dynamic form of
>>> # epsilon sampling that adapts its cutoff probability based on the entropy (high entropy = lower cutoff).
>>> # Pro tip: The paper recomends using `eta_cutoff` values between 3e-4 to 4e-3
>>> outputs ... | 427_7_25 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | A sequence: 1, 2, 3, 4, 5, 6, 7, 8, 9
```
- __call__
[`LogitsProcessor`] that exponentially increases the score of the `eos_token_id` after `start_index` has been
reached. This allows generating shorter sequences without having a hard cutoff, allowing the `eos_token` to be
predicted in a meaningful position.
Args... | 427_7_26 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | This tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where penalty
starts and `decay_factor` represents the factor of exponential decay
eos_token_id (`Union[int, List[int], torch.Tensor]`):
The id(s) of the *end-of-sequence* token.
input_ids_seq_length (`int`):
The length of the inpu... | 427_7_27 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> text = "Just wanted to let you know, I"
>>> inputs = tokenizer(text, return_tensors="pt") | 427_7_28 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Let's consider that we want short sentences, so we limit `max_length=30`. However, we observe that the answer
>>> # tends to end abruptly.
>>> set_seed(1)
>>> outputs = model.generate(**inputs, do_sample=True, temperature=0.9, max_length=30, pad_token_id=50256)
>>> print(tokenizer.batch_decode(outputs)[0])
Just w... | 427_7_29 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # To promote the appearance of the EOS token at the right time, we add the `exponential_decay_length_penalty =
>>> # (start_index, decay_factor)`. Instead of cutting at max_tokens, the output comes to an end before and usually
>>> # with more meaning. What happens is that starting from `start_index` the EOS token s... | 427_7_30 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # ending sequences.
>>> set_seed(1)
>>> outputs = model.generate(
... **inputs,
... do_sample=True,
... temperature=0.9,
... max_length=30,
... pad_token_id=50256,
... exponential_decay_length_penalty=(15, 1.6),
... )
>>> print(tokenizer.batch_decode(outputs)[0])
Just wanted to let you know,... | 427_7_31 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With a small decay factor, you will have a higher chance of getting a meaningful sequence.
>>> set_seed(1)
>>> outputs = model.generate(
... **inputs,
... do_sample=True,
... temperature=0.9,
... max_length=30,
... pad_token_id=50256,
... exponential_decay_length_penalty=(15, 1.01),
... )
... | 427_7_32 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | published in 2010.<|endoftext|>
```
- __call__
[`LogitsProcessor`] that enforces the specified token as the first generated token. Used with encoder-decoder
models.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
Examples:
```python
>>> from transformers import AutoToken... | 427_7_33 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
>>> inputs = tokenizer("Translate from English to German: I love cats.", return_tensors="pt")
>>> # By default, it continues generating according to the model's logits
>>> ou... | 427_7_34 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # We can use `forced_bos_token_id` to force the start of generation with an encoder-decoder model
>>> # (including forcing it to end straight away with an EOS token)
>>> outputs = model.generate(**inputs, max_new_tokens=10, forced_bos_token_id=tokenizer.eos_token_id)
>>> print(tokenizer.batch_decode(outputs)[0])
<p... | 427_7_35 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`Union[int, List[int], torch.Tensor]`):
The id(s) of the *end-of-sequence* token.
device (`str`, *optional*, defaults to `"cpu"`):
The device to allocate the tensors.
Examples:
```python
>>> from transformers import AutoToke... | 427_7_36 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2, 3", return_tensors="pt")
>>> # By default, it continues generating according to the model's logits
>>> outputs = model.generate(**i... | 427_7_37 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # `forced_eos_token_id` ensures the generation ends with a EOS token
>>> outputs = model.generate(**inputs, max_new_tokens=10, forced_eos_token_id=tokenizer.eos_token_id)
>>> print(tokenizer.batch_decode(outputs)[0])
A sequence: 1, 2, 3, 4, 5, 6, 7,<|endoftext|>
```
- __call__
[`LogitsProcessor`] that enforces ... | 427_7_38 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | Note that this logits processor is only effective for [`PreTrainedModel.group_beam_search`]. See [Diverse Beam
Search: Decoding Diverse Solutions from Neural Sequence Models](https://arxiv.org/pdf/1610.02424.pdf) for more
details.
Traditional beam search often generates very similar sequences across different beams.
... | 427_7_39 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | beams in the same time step.
Args:
diversity_penalty (`float`):
This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
particular time. A higher `diversity_penalty` will enforce greater diversity among the beams. Adjusting
this value can help strike a balance betwe... | 427_7_40 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | num_beams (`int`):
Number of beams for beam search. 1 means no beam search.
num_beam_groups (`int`):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
[this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
Examples:
```python
>>> from transformer... | 427_7_41 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Initialize the model and tokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base") | 427_7_42 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # A long text about the solar system
>>> text = (
... "The Solar System is a gravitationally bound system comprising the Sun and the objects that orbit it, "
... "either directly or indirectly. Of the objects that orbit the Sun directly, the largest are the eight "
... "planets, with the remainder being... | 427_7_43 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ... "bodies. The Solar System formed 4.6 billion years ago from the gravitational collapse of a giant "
... "interstellar molecular cloud."
... )
>>> inputs = tokenizer("summarize: " + text, return_tensors="pt") | 427_7_44 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Generate diverse summary
>>> outputs_diverse = model.generate(
... **inputs,
... num_beam_groups=2,
... diversity_penalty=10.0,
... max_length=100,
... num_beams=4,
... num_return_sequences=2,
... )
>>> summaries_diverse = tokenizer.batch_decode(outputs_diverse, skip_special_tokens=True) | 427_7_45 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Generate non-diverse summary
>>> outputs_non_diverse = model.generate(
... **inputs,
... max_length=100,
... num_beams=4,
... num_return_sequences=2,
... )
>>> summary_non_diverse = tokenizer.batch_decode(outputs_non_diverse, skip_special_tokens=True) | 427_7_46 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With `diversity_penalty`, the resulting beams are much more diverse
>>> print(summary_non_diverse)
['the solar system formed 4.6 billion years ago from the collapse of a giant interstellar molecular cloud. of the objects that orbit the Sun directly, the largest are the eight planets.',
'the Solar System formed 4.... | 427_7_47 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> print(summaries_diverse)
['the solar system formed 4.6 billion years ago from the collapse of a giant interstellar molecular cloud. of the objects that orbit the Sun directly, the largest are the eight planets.',
'the solar system formed 4.6 billion years ago from the collapse of a giant interstellar molecular clou... | 427_7_48 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ```
- __call__
[`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method.
This logits processor has no `generate` example, as there shouldn't be a correct co... | 427_7_49 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | its use.
- __call__
[`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
this library doesn't do it (it only does it before, but they may need re-normalization)... | 427_7_50 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2, 3", return_tensors="pt") | 427_7_51 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> inputs = tokenizer("A sequence: 1, 2, 3", return_tensors="pt")
>>> # By default, the scores are not normalized -- the sum of their exponentials is NOT a normalized probability
>>> # distribution, summing to 1
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(torch.a... | 427_7_52 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Normalizing them may have a positive impact on beam methods, or when using the scores on your application
>>> outputs = model.generate(**inputs, renormalize_logits=True, return_dict_in_generate=True, output_scores=True)
>>> print(torch.allclose(torch.sum(torch.exp(outputs.scores[-1])), torch.Tensor((1.000,)), rto... | 427_7_53 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | True
```
- __call__
Abstract base class for all logit processors that can be applied during generation.
- __call__
Abstract base class for all logit processors that can be applied during generation.List
- __call__
[`LogitsProcessor`] enforcing a min-length by setting EOS probability to 0. Note that, for decoder... | 427_7_54 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`Union[int, List[int], torch.Tensor]`):
The id(s) of the *end-of-sequence* token.
device (`str`, *optional*, defaults to `"cpu"`):
The device to allocate the tensors.
Examples:
```python
>>>... | 427_7_55 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer("A number:", return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A nu... | 427_7_56 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # setting `min_length` to a value smaller than the uncontrolled output length has no impact
>>> gen_out = model.generate(**inputs, min_length=3)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A number: one | 427_7_57 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # setting a larger `min_length` will force the model to generate beyond its natural ending point, which is not
>>> # necessarily incorrect
>>> gen_out = model.generate(**inputs, min_length=10)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A number: one thousand, nine hundred and ninety-fou... | 427_7_58 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | - __call__
[`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
Contrarily to [`MinLengthLogitsProcessor`], this processor ignores the prompt.
Args:
prompt_length_to_skip (`int`):
The input tokens length. Not a valid argument when used with `generate` as ... | 427_7_59 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | min_new_tokens (`int`):
The minimum *new* tokens length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`Union[int, List[int], torch.Tensor]`):
The id(s) of the *end-of-sequence* token.
device (`str`, *optional*, defaults to `"cpu"`):
The device to allocate the tensors.
Examples:
``... | 427_7_60 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer(["A number:"], return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A ... | 427_7_61 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # setting `min_new_tokens` will force the model to generate beyond its natural ending point, which is not
>>> # necessarily incorrect
>>> gen_out = model.generate(**inputs, min_new_tokens=2)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A number: one thousand
```
- __call__
[`LogitsPro... | 427_7_62 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | - __call__
[`LogitsProcessor`] that performs min-p, i.e. keeps all tokens that are above a minimum probability, scaled by the
probability of the most likely token. As a result, the filter becomes more agressive in the presence of
high-probability tokens, which is a sign of a confident output that we shouldn't deviate... | 427_7_63 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | [`TopKLogitsWarper`].
Created by @menhguin and @kalomaze (github handles). Code adapted from [this external PR](https://github.com/oobabooga/text-generation-webui/pull/4449/files)
Args:
min_p (`float`):
Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
value b... | 427_7_64 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | the 0.99-0.8 range (use the opposite of normal `top_p` values).
filter_value (`float`, *optional*, defaults to -inf):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
Examples:
```python
>>> from transformer... | 427_7_65 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> set_seed(1)
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = mode... | 427_7_66 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # With `min_p` sampling, the output gets restricted to high-probability tokens.
>>> # Pro tip: In practice, LLMs use `min_p` in the 0.01-0.2 range.
>>> outputs = model.generate(**inputs, do_sample=True, min_p=0.1)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
A sequence: 1, 2, 3, 4, 5, 6, ... | 427_7_67 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ```
- __call__
[`LogitsProcessor`] that enforces that specified sequences will never be selected.
<Tip>
In order to get the token ids of the words that should not appear in the generated text, make sure to set
`add_prefix_space=True` when initializing the tokenizer, and use `tokenizer(bad_words,
add_special_tok... | 427_7_68 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | as fast tokenizers' prefixing behaviours come from `pre tokenizers`. Read more
[here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
</Tip>
Args:
bad_words_ids (`List[List[int]]`):
List of list of token ids that are not allowed to be generated.
eos_token_id (`Union[int, List[int], torch.Tensor]`, *opti... | 427_7_69 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(["In a word, the cake is a"], return_tensors="pt")
>>> output_ids = model.generate(inputs["input_ids"], max_new_tokens=5, pad_token_id=tokenizer.eos_to... | 427_7_70 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Now let's take the bad words out. Please note that the tokenizer is initialized differently
>>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("openai-community/gpt2", add_prefix_space=True) | 427_7_71 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> def get_tokens_as_list(word_list):
... "Converts a sequence of words into a list of tokens"
... tokens_list = []
... for word in word_list:
... tokenized_word = tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0]
... tokens_list.append(tokenized_word)
... retur... | 427_7_72 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> bad_words_ids = get_tokens_as_list(word_list=["mess"])
>>> output_ids = model.generate(
... inputs["input_ids"], max_new_tokens=5, bad_words_ids=bad_words_ids, pad_token_id=tokenizer.eos_token_id
... )
>>> print(tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0])
In a word, the cake is a bit of a s... | 427_7_73 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ```
- __call__
N-grams are groups of "n" consecutive words, characters, or tokens taken from a sequence of text. Given the
sentence: "She runs fast", the bi-grams (n=2) would be ("she", "runs") and ("runs", "fast"). In text generation,
avoiding repetitions of word sequences provides a more diverse output. This [`Lo... | 427_7_74 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | repetition of n-grams by setting the scores of banned tokens to negative infinity which eliminates those tokens
from consideration when further processing the scores. Note that, for decoder-only models like most LLMs, the
prompt is also considered to obtain the n-grams.
[Fairseq](https://github.com/pytorch/fairseq/blob... | 427_7_75 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | <Tip>
Use n-gram penalties with care. For instance, penalizing 2-grams (bigrams) in an article about the city of New York
might lead to undesirable outcomes where the city's name appears only once in the entire text.
[Reference](https://huggingface.co/blog/how-to-generate)
</Tip>
Args:
ngram_size (`int`):
All ngr... | 427_7_76 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer(["Today I"], return_tensors="pt")
>>> output = model.generate(**inputs)
>>> print(tokenizer.decode(output[0], skip_special_tokens=True))
Today I’m not ... | 427_7_77 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Now let's add ngram size using `no_repeat_ngram_size`. This stops the repetitions ("I’m") in the output.
>>> output = model.generate(**inputs, no_repeat_ngram_size=2)
>>> print(tokenizer.decode(output[0], skip_special_tokens=True))
Today I’m not sure if I can get a better understanding of the nature of this issue... | 427_7_78 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ```
- __call__
[`LogitsProcessor`] that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) for more information.
Args:
prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`):
This funct... | 427_7_79 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | This function constraints the beam search to allowed tokens only at each step. This function takes 2
arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the
next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID
`batch_id`. ... | 427_7_80 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer("Alice and Bob", return_tensors="pt")
>>> # By default, it continues generating according to the model's logits
>>> outputs = model.generate(**input... | 427_7_81 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # We can contrain it with `prefix_allowed_tokens_fn` to force a certain behavior based on a prefix.
>>> # For instance, we can force an entire entity to be generated when its beginning is detected.
>>> entity = tokenizer(" Bob Marley", return_tensors="pt").input_ids[0] # 3 tokens
>>> def prefix_allowed_tokens_fn(b... | 427_7_82 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | ... In this case, `batch_id` is not used, but you can set rules for each batch member.
... '''
... if input_ids[-1] == entity[0]:
... return [entity[1].item()]
... elif input_ids[-2] == entity[0] and input_ids[-1] == entity[1]:
... return [entity[2].item()]
... return list(range(toke... | 427_7_83 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> outputs = model.generate(**inputs, max_new_tokens=5, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice and Bob Marley
```
- __call__
[`LogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty ... | 427_7_84 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.
In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a penalty of around
1.2 to achieve a good balance between truthful generation and lack of repetition. To penal... | 427_7_85 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | repetition, use `penalty` values above 1.0, where a higher value penalizes more strongly. To reward and encourage
repetition, use `penalty` values between 0.0 and 1.0, where a lower value rewards more strongly.
Args:
penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 penalizes pr... | 427_7_86 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Initializing the model and tokenizer for it
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer(["I'm not going to"], return_tensors="pt") | 427_7_87 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # This shows a normal generate without any specific parameters
>>> summary_ids = model.generate(**inputs)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
I'm not going to be able to do that. I'm going to be able to do that | 427_7_88 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # This generates a penalty for repeated tokens
>>> penalized_ids = model.generate(**inputs, repetition_penalty=1.1)
>>> print(tokenizer.batch_decode(penalized_ids, skip_special_tokens=True)[0])
I'm not going to be able to do that. I'll just have to go out and play
```
- __call__
[`LogitsProcessor`] that applies... | 427_7_89 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | [`LogitsProcessor`] that applies an additive bias on sequences. The bias is applied to the last token of a sequence
when the next generated token can complete it. Consequently, to take the most of biasing sequences with more than
one token, consider using beam methods (to gracefully work around partially completed sequ... | 427_7_90 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | negative bias) and applying the bias to their prefixes (to ensure the bias is applied earlier).
<Tip>
In order to get the token ids of the sequences that you want to bias, make sure to set `add_prefix_space=True` when
initializing the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. Th... | 427_7_91 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | `add_prefix_space` argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours
come from `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
</Tip>
Args:
sequence_bias (`List[List[Union[List[int], float]]]`):
List of lists that maps a sequ... | 427_7_92 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | [[64], -7.5]]`). Positive biases increase the odds of the
sequence being selected, while negative biases do the opposite. If a sequence has a length of 1, its bias
will always be applied. Otherwise, the bias will only be applied if the sequence in question is about to be
completed (in the token selection step after thi... | 427_7_93 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(["The full name of Donald is Donald"], return_tensors="pt")
>>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4)
>>> print(tokenizer... | 427_7_94 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Now let's control generation through a bias. Please note that the tokenizer is initialized differently!
>>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("openai-community/gpt2", add_prefix_space=True)
>>> def get_tokens(word):
... return tokenizer_with_prefix_space([word], add_special_tokens=... | 427_7_95 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> def get_tokens(word):
... return tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0]
>>> # If we add a negative bias without beam search, it may become "stuck" in a prefix without good continuations
>>> sequence_bias = [get_tokens("Trump"), -10.0]
>>> biased_ids = model.generate(inputs[... | 427_7_96 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald Rumsfeld, | 427_7_97 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # We can also add a positive bias to nudge the model towards specific tokens or continuations
>>> sequence_bias = [get_tokens("Donald Duck"), 10.0]
>>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_speci... | 427_7_98 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | - __call__
[`SuppressTokensAtBeginLogitsProcessor`] supresses a list of tokens as soon as the `generate` function starts
generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` are
not generated at the beginning. Originally created for
[Whisper](https://huggingface... | 427_7_99 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt... | 427_7_100 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Whisper has `begin_suppress_tokens` set by default (= `[220, 50256]`). 50256 is the EOS token, so this means
>>> # it can't generate and EOS token in the first iteration, but it can in the others.
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(outputs.scores[0][... | 427_7_101 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # If we disable `begin_suppress_tokens`, we can generate EOS in the first iteration.
>>> outputs = model.generate(
... **inputs, return_dict_in_generate=True, output_scores=True, begin_suppress_tokens=None
... )
>>> print(outputs.scores[0][0, 50256])
tensor(11.2027)
```
- __call__
This processor can be used... | 427_7_102 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | that they are not generated. Originally created for
[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper).
Examples:
```python
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset | 427_7_103 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt... | 427_7_104 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # Whisper has a long list of suppressed tokens. For instance, in this case, the token 1 is suppressed by default.
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(outputs.scores[1][0, 1]) # 1 (and not 0) is the first freely generated token
tensor(-inf) | 427_7_105 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/internal/generation_utils.md | https://huggingface.co/docs/transformers/en/internal/generation_utils/#pytorch | .md | >>> # If we disable `suppress_tokens`, we can generate it.
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True, suppress_tokens=None)
>>> print(outputs.scores[1][0, 1])
tensor(6.0678)
```
- __call__
Logits processor that implements watermarking techniques for text generation mode... | 427_7_106 |
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