Instructions to use ldm0612/lab1_random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldm0612/lab1_random with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ldm0612/lab1_random") model = AutoModelForSeq2SeqLM.from_pretrained("ldm0612/lab1_random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ldm0612/lab1_random: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/ldm0612/lab1_random/resolve/main/README.md
- Command line
-
hf download hf://ldm0612/lab1_random/README.md
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curl -L -o README.md https://huggingface.co/ldm0612/lab1_random/resolve/main/README.md
1.63 kB
metadata
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: lab1_random
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: 0
lab1_random
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: 9.5630
- Model Preparation Time: 0.0117
- Bleu: 0.0
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0