Text Classification
Transformers
Safetensors
bert
finance
twitter
prediction
ner
named-entity-recognition
turkish
text-embeddings-inference
Instructions to use engibeer/prediction-text-ner-bist30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engibeer/prediction-text-ner-bist30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="engibeer/prediction-text-ner-bist30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("engibeer/prediction-text-ner-bist30") model = AutoModelForSequenceClassification.from_pretrained("engibeer/prediction-text-ner-bist30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- model.safetensors +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 737411072
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:628fb7d48553ccae5f9bd24eb321c8f32ec91c6e98f842c96fe79fa4a6bce3c1
|
| 3 |
size 737411072
|