Summarization
Transformers
PyTorch
Safetensors
Enawené-Nawé
encoder-decoder
text2text-generation
Trained with AutoTrain
Instructions to use chiakya/codebert-gpt2-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chiakya/codebert-gpt2-Summarization 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="chiakya/codebert-gpt2-Summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chiakya/codebert-gpt2-Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("chiakya/codebert-gpt2-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - summarization | |
| language: | |
| - unk | |
| widget: | |
| - text: "I love AutoTrain" | |
| datasets: | |
| - chiakya/autotrain-data-gpt2 | |
| co2_eq_emissions: | |
| emissions: 60.846249800297436 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Summarization | |
| - Model ID: 99861147494 | |
| - CO2 Emissions (in grams): 60.8462 | |
| ## Validation Metrics | |
| - Loss: 1.003 | |
| - Rouge1: 0.100 | |
| - Rouge2: 0.000 | |
| - RougeL: 0.100 | |
| - RougeLsum: 0.100 | |
| - Gen Len: 38.316 | |
| ## Usage | |
| You can use cURL to access this model: | |
| ``` | |
| $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/chiakya/autotrain-gpt2-99861147494 | |
| ``` |