Text Classification
Transformers
Safetensors
English
modernbert
legal
document-ai
page-classification
text-embeddings-inference
Instructions to use RayJackson30/clawbert-149 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RayJackson30/clawbert-149 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RayJackson30/clawbert-149")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RayJackson30/clawbert-149") model = AutoModelForSequenceClassification.from_pretrained("RayJackson30/clawbert-149", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- docxf/docxf.pt +3 -0
- docxf/meta.json +1 -0
docxf/docxf.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5e4cf5e8f820e0c45361960e1160014f1c2e4e8d7f0315646f7d8e34d482b05f
|
| 3 |
+
size 7125314
|
docxf/meta.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"labels": ["body", "cover_page", "subsequent_cover_page", "toc", "toa", "exhibit_cover", "proof_of_service", "verification", "judicial_form", "unknown_other", "transcript"], "emb_dim": 768, "d_model": 256, "heads": 4, "layers": 2, "encoder": "model_modernbert_11 (mean-pooled)", "maxlen": 1536}
|