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[doc] Fix broken links + remove crazy big notebook

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Julien Chaumond
2020-05-07 18:44:18 -04:00
parent 66113bd626
commit c99fe0386b
40 changed files with 52 additions and 52 deletions
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docs/source/examples.md
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@@ -29,7 +29,7 @@ pip install -r ./examples/requirements.txt
## TensorFlow 2.0 Bert models on GLUE
Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py).
Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
@@ -93,7 +93,7 @@ python run_glue_tpu.py \
## Language model training
Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py).
Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
@@ -155,7 +155,7 @@ python run_language_modeling.py \
## Language generation
Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
@@ -364,7 +364,7 @@ Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
```bash
#training on 4 tesla V100(16GB) GPUS
export SWAG_DIR=/path/to/swag_data_dir
python ./examples/run_multiple_choice.py \
python ./examples/multiple-choice/run_multiple_choice.py \
--task_name swag \
--model_name_or_path roberta-base \
--do_train \
@@ -388,7 +388,7 @@ eval_loss = 0.44457291918821606
## SQuAD
Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
#### Fine-tuning BERT on SQuAD1.0
@@ -437,7 +437,7 @@ exact_match = 81.22
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
```bash
python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \
@@ -548,7 +548,7 @@ Larger batch size may improve the performance while costing more memory.
## XNLI
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).