riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

Fix tiny typo (#20841)

Browse Source 
* Fix typo

* Update README.md

* Update run_mlm_flax_stream.py

* Update README.md
This commit is contained in:
fzyzcjy 2022-12-20 16:17:59 +08:00 committed by GitHub
parent 7ef3f19c3c
commit ae3cbbcaf6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
examples/flax/language-modeling/README.md
View File

@ -129,7 +129,7 @@ look at [this](https://colab.research.google.com/github/huggingface/notebooks/bl
In the following, we demonstrate how to train an auto-regressive causal transformer model
in JAX/Flax.
More specifically, we pretrain a randomely initialized [**`gpt2`**](https://huggingface.co/gpt2) model in Norwegian on a single TPUv3-8.
More specifically, we pretrain a randomly initialized [**`gpt2`**](https://huggingface.co/gpt2) model in Norwegian on a single TPUv3-8.
to pre-train 124M [**`gpt2`**](https://huggingface.co/gpt2)
in Norwegian on a single TPUv3-8 pod.

2
examples/research_projects/jax-projects/README.md
View File

@ -710,7 +710,7 @@ class FlaxMLPModel(FlaxMLPPreTrainedModel):
module_class = FlaxMLPModule
```
Now the `FlaxMLPModel` will have a similar interface as PyTorch or Tensorflow models and allows us to attach loaded or randomely initialized weights to the model instance.
Now the `FlaxMLPModel` will have a similar interface as PyTorch or Tensorflow models and allows us to attach loaded or randomly initialized weights to the model instance.
So the important point to remember is that the `model` is not an instance of `nn.Module`; it's an abstract class, like a container that holds a Flax module, its parameters and provides convenient methods for initialization and forward pass. The key take-away here is that an instance of `FlaxMLPModel` is very much stateful now since it holds all the model parameters, whereas the underlying Flax module `FlaxMLPModule` is still stateless. Now to make `FlaxMLPModel` fully compliant with JAX transformations, it is always possible to pass the parameters to `FlaxMLPModel` as well to make it stateless and easier to work with during training. Feel free to take a look at the code to see how exactly this is implemented for ex. [`modeling_flax_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_flax_bert.py#L536)

2
examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py
View File

@ -562,7 +562,7 @@ if __name__ == "__main__":
samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
except StopIteration:
# Once the end of the dataset stream is reached, the training iterator
# is reinitialized and reshuffled and a new eval dataset is randomely chosen.
# is reinitialized and reshuffled and a new eval dataset is randomly chosen.
shuffle_seed += 1
tokenized_datasets.set_epoch(shuffle_seed)

4
tests/generation/test_beam_search.py
View File

@ -59,7 +59,7 @@ class BeamSearchTester:
self.do_early_stopping = do_early_stopping
self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
# cannot be randomely generated
# cannot be randomly generated
self.eos_token_id = vocab_size + 1
def prepare_beam_scorer(self, **kwargs):
@ -283,7 +283,7 @@ class ConstrainedBeamSearchTester:
constraints = [PhrasalConstraint(force_tokens), DisjunctiveConstraint(disjunctive_tokens)]
self.constraints = constraints
# cannot be randomely generated
# cannot be randomly generated
self.eos_token_id = vocab_size + 1
def prepare_constrained_beam_scorer(self, **kwargs):