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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:
```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 \
@@ -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:
```shell
python ./examples/run_generation.py \
python ./examples/text-generation/run_generation.py \
--model_type=gpt2 \
--length=20 \
--model_name_or_path=gpt2 \
@@ -455,7 +455,7 @@ python ./examples/run_generation.py \
and from the Salesforce CTRL model:
```shell
python ./examples/run_generation.py \
python ./examples/text-generation/run_generation.py \
--model_type=ctrl \
--length=20 \
--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,
* 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.

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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).

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.
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
@@ -150,4 +150,4 @@ Example::
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
with the `target_mapping` input.
- 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.
The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.

2
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)
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.
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) |
|---|---|:---:|:---:|:---:|:---:|:---:|
| [`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 | ✅ | ✅ | ✅ | - | - |
| [`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.
# See the License for the specific language governing permissions and
# 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 glob

2
examples/language-modeling/README.md
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@@ -1,7 +1,7 @@
## 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

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
#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 \

4
examples/question-answering/README.md
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@@ -2,7 +2,7 @@
## 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
@@ -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:
```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 \

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

2
examples/text-generation/README.md
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@@ -1,6 +1,6 @@
## 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

4
examples/text-generation/pplm/run_pplm.py
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@@ -17,10 +17,10 @@
"""
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:
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

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
```shell
python3 ./examples/run_squad.py \
python3 ./examples/question-answering/run_squad.py \
--model_type camembert \
--model_name_or_path camembert-base \
--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
```shell
python3 ./examples/run_squad.py \
python3 ./examples/question-answering/run_squad.py \
--model_type camembert \
--model_name_or_path camembert-base \
--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
```shell
python3 ./examples/run_squad.py \
python3 ./examples/question-answering/run_squad.py \
--model_type flaubert \
--model_name_or_path flaubert-base-uncased \
--do_train \

2
model_cards/ktrapeznikov/albert-xlarge-v2-squad-v2/README.md
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@@ -1,5 +1,5 @@
### 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
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
**[`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.

2
model_cards/ktrapeznikov/scibert_scivocab_uncased_squad_v2/README.md
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@@ -1,5 +1,5 @@
### 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
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb

2
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 ✒
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
python run_generation.py \

2
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: ✒
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
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 |
- [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:

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
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_name_or_path dccuchile/bert-base-spanish-wwm-cased \
--do_train \

2
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
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:

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
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:

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
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:

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) 📚
- [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 📋

2
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 |
- [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:

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)
#### [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:

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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 |
- [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:

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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
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:

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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
- [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 📋

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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:
```python
python transformers/examples/run_squad.py \
python transformers/examples/question-answering/run_squad.py \
--model_type distilbert \
--model_name_or_path distilbert-base-multilingual-cased \
--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
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:

2
model_cards/mrm8488/spanbert-finetuned-squadv2/README.md
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@@ -23,7 +23,7 @@ thumbnail:
## Model training
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:

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src/transformers/modeling_albert.py
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@@ -932,7 +932,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
Examples::
# 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
import torch

2
src/transformers/modeling_roberta.py
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@@ -643,7 +643,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel):
Examples::
# 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
import torch

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src/transformers/modeling_tf_albert.py
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@@ -865,7 +865,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel):
Examples::
# 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
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::
# 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
from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering