Feature Extraction
Transformers
Safetensors
sentence-transformers
embeddings
lora
sociology
retrieval
Instructions to use CodeSoulco/THETA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeSoulco/THETA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CodeSoulco/THETA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CodeSoulco/THETA", dtype="auto") - sentence-transformers
How to use CodeSoulco/THETA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CodeSoulco/THETA") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Delete theta/lora/logs/mental_health_4B_supervised_epoch_loss.txt with huggingface_hub
Browse files
theta/lora/logs/mental_health_4B_supervised_epoch_loss.txt
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
Epoch 1/3 | Time: 49012.8s | Loss: 0.6594 | Accuracy: 0.8020
|
| 2 |
-
Epoch 2/3 | Time: 52143.9s | Loss: 0.6012 | Accuracy: 0.8287
|
| 3 |
-
Epoch 3/3 | Time: 49382.2s | Loss: 0.5624 | Accuracy: 0.8400
|
|
|
|
|
|
|
|
|
|
|
|