Instructions to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary") model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update test_results.txt
Browse files- test_results.txt +4 -0
test_results.txt
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
eval_loss = 0.40332261432138866
|
| 2 |
+
eval_acc = 0.9240561896400351
|
| 3 |
+
eval_f1 = 0.9242900148212283
|
| 4 |
+
eval_acc_and_f1 = 0.9241731022306316
|