Text Classification
Transformers
Safetensors
English
modernbert
sentiment-analysis
sentiment
sst-2
sst2
reviews
english
positive-negative
distilbert-sst2-alternative
text-embeddings-inference
Instructions to use AnkitAI/Sensible-ModernBERT-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnkitAI/Sensible-ModernBERT-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnkitAI/Sensible-ModernBERT-Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnkitAI/Sensible-ModernBERT-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("AnkitAI/Sensible-ModernBERT-Sentiment-Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload eval/result.json with huggingface_hub
Browse files- eval/result.json +8 -0
eval/result.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"base": "answerdotai/ModernBERT-base",
|
| 3 |
+
"seed": 42,
|
| 4 |
+
"task": "sst2",
|
| 5 |
+
"val_accuracy": 0.9461009174311926,
|
| 6 |
+
"val_f1": 0.9471316085489314,
|
| 7 |
+
"incumbent": "distilbert-sst2 = 0.913 on this same validation split"
|
| 8 |
+
}
|