|
|
|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- oscar-corpus/OSCAR-2109 |
|
language: |
|
- es |
|
- en |
|
pipeline_tag: text-generation |
|
library_name: transformers |
|
--- |
|
|
|
# B-GPT_es_en_simultaneous |
|
|
|
This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on Spanish data. In the second half of training, the model was trained on a 50%-50% mix of Spanish and English data. At the end of training, 75% of training data seen by the model is Spanish and 25% is English. The tokenizer was trained on the same overall proportions of data as the language model at the final step. |
|
|
|
## Model details: |
|
|
|
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. |
|
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! |
|
Details for this model specifically: |
|
|
|
* Architecture: gpt2 |
|
* Parameters: 124770816 |
|
* Maximum sequence length: 512 tokens |
|
* Training tokens: 12B |
|
* Vocabulary size: 50000 |
|
* Compute cost: ~9 NVIDIA A6000 GPU hours |
|
* CO2 Emission: 1.17 kg |
|
|
|
Training dataset: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) |
|
|
|
Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000. |
|
|
|
## Use This Model |
|
|
|
Load the model: |
|
|
|
Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision. |
|
|
|
``` |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_es_en_simultaneous") |
|
model = AutoModel.from_pretrained("catherinearnett/B-GPT_es_en_simultaneous", revision = "128000") |
|
|
|
|
|
```` |
|
|
|
Text Generation: |
|
|
|
``` |
|
from transformers import pipeline |
|
|
|
pipe = pipeline("text-generation", model="catherinearnett/B-GPT_es_en_simultaneous") |
|
|
|
pipe("I am a") |
|
|
|
``` |
|
|
|
## Citation |
|
|
|
If you use this model, please cite: |
|
|
|
``` |
|
|
|
|
|
``` |
|
|
