Instructions to use saicharan8/telugu-summarization-umt5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saicharan8/telugu-summarization-umt5-small 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="saicharan8/telugu-summarization-umt5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("saicharan8/telugu-summarization-umt5-small") model = AutoModelForSeq2SeqLM.from_pretrained("saicharan8/telugu-summarization-umt5-small") - Notebooks
- Google Colab
- Kaggle
output
This model is a fine-tuned version of google/umt5-small on an Telugu-News Article Summaries 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for saicharan8/telugu-summarization-umt5-small
Base model
google/umt5-small