Text Classification
Transformers
PyTorch
English
bert
sentiment-analysis
finance
Eval Results (legacy)
text-embeddings-inference
Instructions to use Kroalist/financial-sentiment-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kroalist/financial-sentiment-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kroalist/financial-sentiment-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kroalist/financial-sentiment-bert") model = AutoModelForSequenceClassification.from_pretrained("Kroalist/financial-sentiment-bert") - 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:efe56d0799f7f80c6d3351d73dba778c7ced97ca2725f0f86528e32160bab404
|
| 3 |
+
size 437965908
|