Text Classification
Transformers
Safetensors
distilbert
sentiment
ml-observability
text-embeddings-inference
Instructions to use karaboLLM/sentiment-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karaboLLM/sentiment-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karaboLLM/sentiment-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karaboLLM/sentiment-distilbert") model = AutoModelForSequenceClassification.from_pretrained("karaboLLM/sentiment-distilbert") - Notebooks
- Google Colab
- Kaggle
File size: 1,191 Bytes
abb57de da82f48 abb57de da82f48 abb57de da82f48 abb57de da82f48 4067701 da82f48 4067701 da82f48 4067701 da82f48 abb57de da82f48 4067701 da82f48 4067701 da82f48 4067701 da82f48 abb57de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | {
"n_samples": 2000,
"class_proportions": {
"negative": 0.494,
"positive": 0.506
},
"confidence_mean": 0.9148861765861511,
"confidence_std": 0.1137993112206459,
"confidence_histogram": {
"bins": [
0.0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6000000000000001,
0.7000000000000001,
0.8,
0.9,
1.0
],
"counts": [
0,
0,
0,
0,
0,
80,
87,
108,
192,
1533
]
},
"prediction_histogram": {
"neg_bins": [
0.0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6000000000000001,
0.7000000000000001,
0.8,
0.9,
1.0
],
"neg_counts": [
771,
100,
55,
44,
42,
38,
43,
53,
92,
762
],
"pos_bins": [
0.0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6000000000000001,
0.7000000000000001,
0.8,
0.9,
1.0
],
"pos_counts": [
762,
92,
53,
43,
38,
42,
44,
55,
100,
771
]
}
} |