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[BIG] pytorch-transformers => transformers

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thomwolf
2019-09-26 10:15:53 +02:00
parent 2f071fcb02
commit 31c23bd5ee
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docs/source/quickstart.md
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@@ -2,7 +2,7 @@
## Philosophy
PyTorch-Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
The library was designed with two strong goals in mind:
@@ -39,7 +39,7 @@ The library is build around three type of classes for each models:
All these classes can be instantiated from pretrained instances and saved locally using two methods:
- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/pytorch-transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
- `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized in two parts:
@@ -59,7 +59,7 @@ Let's start by preparing a tokenized input (a list of token embeddings indices t
```python
import torch
from pytorch_transformers import BertTokenizer, BertModel, BertForMaskedLM
from transformers import BertTokenizer, BertModel, BertForMaskedLM
# OPTIONAL: if you want to have more information on what's happening under the hood, activate the logger as follows
import logging
@@ -106,7 +106,7 @@ model.to('cuda')
with torch.no_grad():
# See the models docstrings for the detail of the inputs
outputs = model(tokens_tensor, token_type_ids=segments_tensors)
# PyTorch-Transformers models always output tuples.
# Transformers models always output tuples.
# See the models docstrings for the detail of all the outputs
# In our case, the first element is the hidden state of the last layer of the Bert model
encoded_layers = outputs[0]
@@ -145,7 +145,7 @@ First let's prepare a tokenized input from our text string using `GPT2Tokenizer`
```python
import torch
from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging