Summarization
Transformers
PyTorch
English
t5
text2text-generation
wikihow
t5-small
lm-head
seq2seq
pipeline:summarization
text-generation-inference
Instructions to use deep-learning-analytics/wikihow-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deep-learning-analytics/wikihow-t5-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="deep-learning-analytics/wikihow-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("deep-learning-analytics/wikihow-t5-small") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Model description
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This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.5.
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We have written a blog post that covers the training procedure. Please find it here.
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## Usage
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## Model description
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This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.5.
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We have written a blog post that covers the training procedure. Please find it [here](https://medium.com/@priya.dwivedi/fine-tuning-a-t5-transformer-for-any-summarization-task-82334c64c81).
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## Usage
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