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@ -19,7 +19,7 @@ This implementation is provided with [Google's pre-trained models](https://githu
## Installation
This repo was tested on Python 3.6+ and PyTorch 0.4.1
This repo was tested on Python 3.5+ and PyTorch 0.4.1/1.0.0
### With pip
@ -372,9 +372,9 @@ Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your mach
We showcase several fine-tuning examples based on (and extended from) [the original implementation](https://github.com/google-research/bert/):
- a sequence-level classifier on the MRPC classification corpus,
- a token-level classifier on the question answering dataset SQuAD, and
- a sequence-level multiple-choice classifier on the SWAG classification corpus.
- a *sequence-level classifier* on the MRPC classification corpus,
- a *token-level classifier* on the question answering dataset SQuAD, and
- a *sequence-level multiple-choice classifier* on the SWAG classification corpus.
#### MRPC
@ -427,7 +427,7 @@ python run_classifier.py \
#### SQuAD
This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on single tesla V100 16GB.
This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB.
The data for SQuAD can be downloaded with the following links and should be saved in a `$SQUAD_DIR` directory.
@ -458,7 +458,9 @@ Training with the previous hyper-parameters gave us the following results:
{"f1": 88.52381567990474, "exact_match": 81.22043519394512}
```
The data for Swag can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf)
#### SWAG
The data for SWAG can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf)
```shell
export SWAG_DIR=/path/to/SWAG