Instructions to use TinaLiHF/fined-tuned-T5small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TinaLiHF/fined-tuned-T5small 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="TinaLiHF/fined-tuned-T5small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TinaLiHF/fined-tuned-T5small") model = AutoModelForSeq2SeqLM.from_pretrained("TinaLiHF/fined-tuned-T5small") - Notebooks
- Google Colab
- Kaggle
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- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
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license: openrail datasets:
- multi_news language:
- en
Model Card for Model ID
Model Details
This is developed for the TLDR project of ANLP.
This is fine-tuned T5 small model with the Multi_news dataset, with adam optimiser.
Aim to summarise long articles into shorten summaries
Model Description
- Developed by: Li, T
- Shared by [optional]: [More Information Needed]
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- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: https://huggingface.co/t5-small
Model Sources [optional]
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Uses
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: NVIDIA GeForce RTX 3060 Laptop GPU
- Hours used: 3:06:45 Hr
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Dataset used to train TinaLiHF/fined-tuned-T5small
Paper for TinaLiHF/fined-tuned-T5small
Evaluation results
- ROUGE-1 on multi_newsvalidation set self-reported15.280
- ROUGE-2 on multi_newsvalidation set self-reported15.070
- ROUGE-L on multi_newsvalidation set self-reported1.680
- ROUGE-LSUM on multi_newsvalidation set self-reported13.460