Translation
Transformers
PyTorch
TensorBoard
marian
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use shed-e/Translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/Translation with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="shed-e/Translation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shed-e/Translation") model = AutoModelForSeq2SeqLM.from_pretrained("shed-e/Translation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shed-e/Translation: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/shed-e/Translation/resolve/main/README.md
- Command line
-
hf download hf://shed-e/Translation/README.md
-
curl -L -o README.md https://huggingface.co/shed-e/Translation/resolve/main/README.md
1.52 kB
metadata
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.83113187001415
marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8311
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1