Instructions to use vikp/pdf_postprocessor_t5_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/pdf_postprocessor_t5_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/pdf_postprocessor_t5_base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor_t5_base") model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor_t5_base") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
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:b69ed337e5c0bd2eed1261d08e32fe389f7535c5814c1ea263d23a767b0cbe02
|
| 3 |
+
size 1658858808
|