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Just re-reading the whole doc every couple of months 😬 (#18489)

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* Delete valohai.yaml

* NLP => ML

* typo

* website supports https

* datasets

* 60k + modalities

* unrelated link fixing for accelerate

* Ok those links were actually broken

* Fix link

* Make `AutoTokenizer` auto-link

* wording tweak

* add at least one non-nlp task
This commit is contained in:
Julien Chaumond
2022-08-06 09:38:55 +02:00
committed by GitHub
parent b8c247b6d0
commit 8d1f9039d0
20 changed files with 39 additions and 130 deletions
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2
examples/legacy/pytorch-lightning/run_ner.sh
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@@ -5,7 +5,7 @@ pip install -r ../requirements.txt
## The relevant files are currently on a shared Google
## drive at https://drive.google.com/drive/folders/1kC0I2UGl2ltrluI9NqDjaQJGw5iliw_J
## Monitor for changes and eventually migrate to nlp dataset
## Monitor for changes and eventually migrate to use the `datasets` library
curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \
| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \

2
examples/legacy/token-classification/README.md
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@@ -291,4 +291,4 @@ On the test dataset the following results could be achieved:
05/29/2020 23:34:02 - INFO - __main__ - eval_f1 = 0.47440836543753434
```
WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](http://nlpprogress.com/english/named_entity_recognition.html).
WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](https://nlpprogress.com/english/named_entity_recognition.html).

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examples/legacy/token-classification/run.sh
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@@ -1,6 +1,6 @@
## The relevant files are currently on a shared Google
## drive at https://drive.google.com/drive/folders/1kC0I2UGl2ltrluI9NqDjaQJGw5iliw_J
## Monitor for changes and eventually migrate to nlp dataset
## Monitor for changes and eventually migrate to use the `datasets` library
curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \
| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \

4
examples/pytorch/README.md
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@@ -15,12 +15,12 @@ limitations under the License.
# Examples
This folder contains actively maintained examples of use of 🤗 Transformers using the PyTorch backend, organized along NLP tasks.
This folder contains actively maintained examples of use of 🤗 Transformers using the PyTorch backend, organized by ML task.
## The Big Table of Tasks
Here is the list of all our examples:
- with information on whether they are **built on top of `Trainer``** (if not, they still work, they might
- with information on whether they are **built on top of `Trainer`** (if not, they still work, they might
just lack some features),
- whether or not they have a version using the [🤗 Accelerate](https://github.com/huggingface/accelerate) library.
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.

2
examples/tensorflow/README.md
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@@ -15,7 +15,7 @@ limitations under the License.
# Examples
This folder contains actively maintained examples of use of 🤗 Transformers organized into different NLP tasks. All examples in this folder are **TensorFlow** examples, and are written using native Keras rather than classes like `TFTrainer`, which we now consider deprecated. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement!
This folder contains actively maintained examples of use of 🤗 Transformers organized into different ML tasks. All examples in this folder are **TensorFlow** examples, and are written using native Keras rather than classes like `TFTrainer`, which we now consider deprecated. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement!
In addition, all scripts here now support the [🤗 Datasets](https://github.com/huggingface/datasets) library - you can grab entire datasets just by changing one command-line argument!