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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
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6
README.md
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@@ -414,7 +414,7 @@ Training with these hyper-parameters gave us the following results:
This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD: This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
```bash ```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_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \ --model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \ --do_train \
@@ -447,7 +447,7 @@ The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-g
Here is how to run the script with the small version of OpenAI GPT-2 model: Here is how to run the script with the small version of OpenAI GPT-2 model:
```shell ```shell
python ./examples/run_generation.py \ python ./examples/text-generation/run_generation.py \
--model_type=gpt2 \ --model_type=gpt2 \
--length=20 \ --length=20 \
--model_name_or_path=gpt2 \ --model_name_or_path=gpt2 \
@@ -455,7 +455,7 @@ python ./examples/run_generation.py \
and from the Salesforce CTRL model: and from the Salesforce CTRL model:
```shell ```shell
python ./examples/run_generation.py \ python ./examples/text-generation/run_generation.py \
--model_type=ctrl \ --model_type=ctrl \
--length=20 \ --length=20 \
--model_name_or_path=ctrl \ --model_name_or_path=ctrl \

2
docs/source/bertology.rst
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@@ -15,4 +15,4 @@ In order to help this new field develop, we have included a few additional featu
* accessing all the attention weights for each head of BERT/GPT/GPT-2, * accessing all the attention weights for each head of BERT/GPT/GPT-2,
* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650. * retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE. To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.

14
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 ## 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/). 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 ## 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 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 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 ## 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. 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 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 ```bash
#training on 4 tesla V100(16GB) GPUS #training on 4 tesla V100(16GB) GPUS
export SWAG_DIR=/path/to/swag_data_dir 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 \ --task_name swag \
--model_name_or_path roberta-base \ --model_name_or_path roberta-base \
--do_train \ --do_train \
@@ -388,7 +388,7 @@ eval_loss = 0.44457291918821606
## SQuAD ## 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 #### 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: 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 ```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_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \ --model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \ --do_train \
@@ -548,7 +548,7 @@ Larger batch size may improve the performance while costing more memory.
## XNLI ## 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). [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).

4
docs/source/main_classes/processors.rst
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@@ -74,7 +74,7 @@ This library hosts the processor to load the XNLI data:
Please note that since the gold labels are available on the test set, evaluation is performed on the test set. Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the An example using these processors is given in the
`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script. `run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
SQuAD SQuAD
@@ -150,4 +150,4 @@ Example::
Another example using these processors is given in the Another example using these processors is given in the
`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/run_squad.py>`__ script. `run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.

2
docs/source/model_doc/xlnet.rst
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@@ -29,7 +29,7 @@ Tips:
XLNet is pretrained using only a sub-set of the output tokens as target which are selected XLNet is pretrained using only a sub-set of the output tokens as target which are selected
with the `target_mapping` input. with the `target_mapping` input.
- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and - To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
`target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`) `target_mapping` inputs to control the attention span and outputs (see examples in `examples/text-generation/run_generation.py`)
- XLNet is one of the few models that has no sequence length limit. - XLNet is one of the few models that has no sequence length limit.
The original code can be found `here <https://github.com/zihangdai/xlnet/>`_. The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.

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docs/source/multilingual.rst
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@@ -80,7 +80,7 @@ You can then feed it all as input to your model:
outputs = model(input_ids, langs=langs) outputs = model(input_ids, langs=langs)
The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/run_generation.py>`__ The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__
can generate text using the CLM checkpoints from XLM, using the language embeddings. can generate text using the CLM checkpoints from XLM, using the language embeddings.
XLM without Language Embeddings XLM without Language Embeddings

2
examples/README.md
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@@ -17,7 +17,7 @@ This is still a work-in-progress – in particular documentation is still sparse
| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | One-click Deploy to Azure (wip) | | Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | One-click Deploy to Azure (wip) |
|---|---|:---:|:---:|:---:|:---:|:---:| |---|---|:---:|:---:|:---:|:---:|:---:|
| [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - | - | | [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - | - |
| [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) | | [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) |
| [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | - | | [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | - |
| [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | - | - | - | - | | [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | - | - | - | - |

2
examples/distillation/run_squad_w_distillation.py
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@@ -13,7 +13,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
""" This is the exact same script as `examples/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation.""" """ This is the exact same script as `examples/question-answering/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation."""
import argparse import argparse
import glob import glob

2
examples/language-modeling/README.md
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@@ -1,7 +1,7 @@
## Language model training ## 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 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 to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa

2
examples/multiple-choice/README.md
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@@ -8,7 +8,7 @@ Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
```bash ```bash
#training on 4 tesla V100(16GB) GPUS #training on 4 tesla V100(16GB) GPUS
export SWAG_DIR=/path/to/swag_data_dir 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 \ --task_name swag \
--model_name_or_path roberta-base \ --model_name_or_path roberta-base \
--do_train \ --do_train \

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examples/question-answering/README.md
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@@ -2,7 +2,7 @@
## SQuAD ## 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 #### Fine-tuning BERT on SQuAD1.0
@@ -51,7 +51,7 @@ 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: 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 ```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_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \ --model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \ --do_train \

4
examples/text-classification/README.md
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@@ -2,7 +2,7 @@
# Run TensorFlow 2.0 version # Run TensorFlow 2.0 version
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/). 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/).
@@ -256,7 +256,7 @@ TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recal
# XNLI # 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). [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).

2
examples/text-generation/README.md
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@@ -1,6 +1,6 @@
## Language generation ## 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. 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 A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you

4
examples/text-generation/pplm/run_pplm.py
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@@ -17,10 +17,10 @@
""" """
Example command with bag of words: Example command with bag of words:
python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
Example command with discriminator: Example command with discriminator:
python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95 python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
""" """
import argparse import argparse

2
model_cards/fmikaelian/camembert-base-fquad/README.md
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@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([CamemBERT](https://camembert
## Training hyperparameters ## Training hyperparameters
```shell ```shell
python3 ./examples/run_squad.py \ python3 ./examples/question-answering/run_squad.py \
--model_type camembert \ --model_type camembert \
--model_name_or_path camembert-base \ --model_name_or_path camembert-base \
--do_train \ --do_train \

2
model_cards/fmikaelian/camembert-base-squad/README.md
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@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([CamemBERT](https://camembert
## Training hyperparameters ## Training hyperparameters
```shell ```shell
python3 ./examples/run_squad.py \ python3 ./examples/question-answering/run_squad.py \
--model_type camembert \ --model_type camembert \
--model_name_or_path camembert-base \ --model_name_or_path camembert-base \
--do_train \ --do_train \

2
model_cards/fmikaelian/flaubert-base-uncased-squad/README.md
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@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([flaubert](https://github.com
## Training hyperparameters ## Training hyperparameters
```shell ```shell
python3 ./examples/run_squad.py \ python3 ./examples/question-answering/run_squad.py \
--model_type flaubert \ --model_type flaubert \
--model_name_or_path flaubert-base-uncased \ --model_name_or_path flaubert-base-uncased \
--do_train \ --do_train \

2
model_cards/ktrapeznikov/albert-xlarge-v2-squad-v2/README.md
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@@ -1,5 +1,5 @@
### Model ### Model
**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)** **[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters ### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb

2
model_cards/ktrapeznikov/biobert_v1.1_pubmed_squad_v2/README.md
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@@ -1,5 +1,5 @@
### Model ### Model
**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)** **[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
This model is cased. This model is cased.

2
model_cards/ktrapeznikov/scibert_scivocab_uncased_squad_v2/README.md
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@@ -1,5 +1,5 @@
### Model ### Model
**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)** **[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters ### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb

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model_cards/mrm8488/GPT-2-finetuned-CORD19/README.md
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@@ -40,7 +40,7 @@ python run_language_modeling.py \
## Model in action / Example of usage ✒ ## Model in action / Example of usage ✒
You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py) You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py)
```bash ```bash
python run_generation.py \ python run_generation.py \

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model_cards/mrm8488/GPT-2-finetuned-covid-bio-medrxiv/README.md
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@@ -37,7 +37,7 @@ python run_language_modeling.py \
## Model in action / Example of usage: ✒ ## Model in action / Example of usage: ✒
You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py) You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py)
```bash ```bash
python run_generation.py \ python run_generation.py \

2
model_cards/mrm8488/TinyBERT-spanish-uncased-finetuned-ner/README.md
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@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
| Dev | 2.2 K | | Dev | 2.2 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) - [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
- Labels covered: - Labels covered:

2
model_cards/mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md
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@@ -29,7 +29,7 @@ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following com
```bash ```bash
export SQUAD_DIR=path/to/nl_squad export SQUAD_DIR=path/to/nl_squad
python transformers/examples/run_squad.py \ python transformers/examples/question-answering/run_squad.py \
--model_type bert \ --model_type bert \
--model_name_or_path dccuchile/bert-base-spanish-wwm-cased \ --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
--do_train \ --do_train \

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model_cards/mrm8488/bert-medium-finetuned-squadv2/README.md
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@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

2
model_cards/mrm8488/bert-mini-finetuned-squadv2/README.md
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@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

2
model_cards/mrm8488/bert-small-finetuned-squadv2/README.md
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@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

2
model_cards/mrm8488/bert-small-finetuned-typo-detection/README.md
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@@ -11,7 +11,7 @@ thumbnail:
- Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚 - Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚
- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) 🏋️‍♂️ - [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) 🏋️‍♂️
## Metrics on test set 📋 ## Metrics on test set 📋

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model_cards/mrm8488/bert-spanish-cased-finetuned-ner/README.md
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@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
| Dev | 2.2 K | | Dev | 2.2 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) - [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
- Labels covered: - Labels covered:

2
model_cards/mrm8488/bert-spanish-cased-finetuned-pos-syntax/README.md
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@@ -11,7 +11,7 @@ This model is a fine-tuned version of the Spanish BERT [(BETO)](https://github.c
- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) - [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
#### [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) #### [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
#### 21 Syntax annotations (Labels) covered: #### 21 Syntax annotations (Labels) covered:

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model_cards/mrm8488/bert-spanish-cased-finetuned-pos/README.md
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@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
| Dev | 50 K | | Dev | 50 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) - [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
- **60** Labels covered: - **60** Labels covered:

2
model_cards/mrm8488/bert-tiny-finetuned-squadv2/README.md
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@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

2
model_cards/mrm8488/distilbert-base-multi-cased-finetuned-typo-detection/README.md
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@@ -11,7 +11,7 @@ thumbnail:
- Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚 for 15 languages - Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚 for 15 languages
- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) 🏋️‍♂️ - [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) 🏋️‍♂️
## Metrics on test set 📋 ## Metrics on test set 📋

2
model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md
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@@ -31,7 +31,7 @@ The model was fine-tuned on a Tesla P100 GPU and 25GB of RAM.
The script is the following: The script is the following:
```python ```python
python transformers/examples/run_squad.py \ python transformers/examples/question-answering/run_squad.py \
--model_type distilbert \ --model_type distilbert \
--model_name_or_path distilbert-base-multilingual-cased \ --model_name_or_path distilbert-base-multilingual-cased \
--do_train \ --do_train \

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model_cards/mrm8488/spanbert-finetuned-squadv1/README.md
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@@ -26,7 +26,7 @@ thumbnail:
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

2
model_cards/mrm8488/spanbert-finetuned-squadv2/README.md
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@@ -23,7 +23,7 @@ thumbnail:
## Model training ## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM. The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
## Results: ## Results:

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src/transformers/modeling_albert.py
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@@ -932,7 +932,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
Examples:: Examples::
# The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
# examples/run_squad.py example to see how to fine-tune a model to a question answering task. # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
from transformers import AlbertTokenizer, AlbertForQuestionAnswering from transformers import AlbertTokenizer, AlbertForQuestionAnswering
import torch import torch

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src/transformers/modeling_roberta.py
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@@ -643,7 +643,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel):
Examples:: Examples::
# The checkpoint roberta-large is not fine-tuned for question answering. Please see the # The checkpoint roberta-large is not fine-tuned for question answering. Please see the
# examples/run_squad.py example to see how to fine-tune a model to a question answering task. # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
from transformers import RobertaTokenizer, RobertaForQuestionAnswering from transformers import RobertaTokenizer, RobertaForQuestionAnswering
import torch import torch

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src/transformers/modeling_tf_albert.py
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@@ -865,7 +865,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel):
Examples:: Examples::
# The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
# examples/run_squad.py example to see how to fine-tune a model to a question answering task. # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
import tensorflow as tf import tensorflow as tf
from transformers import AlbertTokenizer, TFAlbertForQuestionAnswering from transformers import AlbertTokenizer, TFAlbertForQuestionAnswering

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src/transformers/modeling_tf_roberta.py
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@@ -481,7 +481,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel):
Examples:: Examples::
# The checkpoint roberta-base is not fine-tuned for question answering. Please see the # The checkpoint roberta-base is not fine-tuned for question answering. Please see the
# examples/run_squad.py example to see how to fine-tune a model to a question answering task. # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
import tensorflow as tf import tensorflow as tf
from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering