lm_hf: A Python repository from Tikquuss

1. Setting

git clone https://github.com/Tikquuss/lm_hf
cd lm_hf

python3 -m install pip
pip install -r requirements.txt

2. Build a tokenizer from scratch if you are not going to use a pre-trained model (supports txt and csv)

See tokenizing.py for all other parameters (and descriptions).

st=my/save/path
mkdir -p $st

datapath=/path/to/data
text_column=text
python -m src.tokenizing -fe gpt2 -p ${datapath}/data_train.csv,${datapath}/data_val.csv,${datapath}/data_test.csv -vs 25000 -mf 2 -st $st -tc $text_column

#python -m src.tokenizing -fe bert-base-uncased -p wikitext -dn wikitext-2-raw-v1 --vocab_size 25000 -st $st

# ...

The tokenizer will be saved in ${save_to}/tokenizer.pt.

3. Dictionary (work, but deprecated for the moment)

You can, instead of pre-training a tokenizer, build a simple vocabulary (by dividing the sentences according to the whitespace character - default option, or by dividing sentences into phonemes, ...), then build the tokenizer with this vocabulary during the training/evaluation (tokenizer_params="vocab_file=str(${save_to}/word_to_id.txt),t_class=str(bert_tokenizer),...").

st=my/save/path
mkdir -p $st

datapath=/path/to/data
text_column=text

python -m src.utils -p ${datapath}/data_train.csv,${datapath}/data_val.csv,${datapath}/data_test.csv -st $st -tc $text_column

But this option is not recommended for the moment (any deep sanitary check has been done so far).

4. Train and/or evaluate a model (from scratch or from a pre-trained model and/or tokenizer)

See trainer.py and train.sh for all other parameters (and descriptions)

. train.sh

5. TensorBoard (visualize the evolution of the loss/acc/... per step/epoch/...)

See https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html

%load_ext tensorboard

%tensorboard --logdir ${log_dir}/${task}/lightning_logs

6. Prediction

To generate texts or fill the masks, you have to use the predict_params parameter. By default, this will be done on the test dataset (or the dataset specified with the split parameter), but it is better to put your examples in a text file, csv, json (...) and use it instead of the test dataset (test_data_files parameter). Don't forget to set the group_texts parameter to False in this case, and make sure that the length of the prompts or sentences (and the value of the max_length parameter) does not exceed the value of the max_position_embeddings/n_positions/ ... parameter of your model.

For example for text generation, the file can be in the following form:
- for a text file :
```
prompt 1
prompt 2
...
```
- for a csv file :
```
text_column | ...
prompt 1    | ...
prompt 2    | ...
...	    | ...
```
For the mask filling, replace the prompts below by the sentences on which to do the MLM

The result will be stored by default in the ${log_dir}/${task}/predict.txt file, but you can change this path by adding this value to the predict_params parameter:

predict_params="...,output_file=str(my_path/file.txt),..."