Instructions to use dadashzadeh/mbart-finetuned-fa-pretrained-mmad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dadashzadeh/mbart-finetuned-fa-pretrained-mmad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="dadashzadeh/mbart-finetuned-fa-pretrained-mmad")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dadashzadeh/mbart-finetuned-fa-pretrained-mmad") model = AutoModelForSeq2SeqLM.from_pretrained("dadashzadeh/mbart-finetuned-fa-pretrained-mmad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mbart-finetuned-fa-pretrained-mmad
This model is a fine-tuned version of eslamxm/mbart-finetuned-fa on an unknown dataset.
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: 0
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
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