Summarization
PEFT
Safetensors
Transformers
English
lora
abstractive-summarization
Generated from Trainer
Instructions to use lityops/Abstractive-Style-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lityops/Abstractive-Style-Summarizer with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "lityops/Abstractive-Style-Summarizer") - Transformers
How to use lityops/Abstractive-Style-Summarizer 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="lityops/Abstractive-Style-Summarizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lityops/Abstractive-Style-Summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -23,7 +23,7 @@ This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.
|
|
| 23 |
|
| 24 |
- **Model type:** Sequence-to-Sequence Transformer (T5)
|
| 25 |
- **Language(s):** English
|
| 26 |
-
- **License:**
|
| 27 |
- **Finetuned from model:** google/flan-t5-base
|
| 28 |
- **Training Method:** PEFT (LoRA)
|
| 29 |
|
|
|
|
| 23 |
|
| 24 |
- **Model type:** Sequence-to-Sequence Transformer (T5)
|
| 25 |
- **Language(s):** English
|
| 26 |
+
- **License:** MIT
|
| 27 |
- **Finetuned from model:** google/flan-t5-base
|
| 28 |
- **Training Method:** PEFT (LoRA)
|
| 29 |
|