Summarization
Transformers
PyTorch
English
t5
text2text-generation
medical
text-generation-inference
Instructions to use xtie/T5Score-PET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtie/T5Score-PET 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="xtie/T5Score-PET")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xtie/T5Score-PET") model = AutoModelForSeq2SeqLM.from_pretrained("xtie/T5Score-PET") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:911e666cb13683ce5249d75242093b486a72a77f0f9524aa3ace44b12fd45f7e
|
| 3 |
+
size 3132668896
|