Instructions to use POKWIR/bert-emotions-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use POKWIR/bert-emotions-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="POKWIR/bert-emotions-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("POKWIR/bert-emotions-classifier") model = AutoModelForSequenceClassification.from_pretrained("POKWIR/bert-emotions-classifier") - Notebooks
- Google Colab
- Kaggle
labels
Browse files- config.json +9 -9
config.json
CHANGED
|
@@ -23,15 +23,15 @@
|
|
| 23 |
"initializer_range": 0.02,
|
| 24 |
"intermediate_size": 3072,
|
| 25 |
"label2id": {
|
| 26 |
-
"
|
| 27 |
-
"
|
| 28 |
-
"
|
| 29 |
-
"
|
| 30 |
-
"
|
| 31 |
-
"
|
| 32 |
-
"
|
| 33 |
-
"
|
| 34 |
-
"
|
| 35 |
},
|
| 36 |
"layer_norm_eps": 1e-12,
|
| 37 |
"max_position_embeddings": 512,
|
|
|
|
| 23 |
"initializer_range": 0.02,
|
| 24 |
"intermediate_size": 3072,
|
| 25 |
"label2id": {
|
| 26 |
+
"Gratitude"": 0,
|
| 27 |
+
"Satisfaction"": 1,
|
| 28 |
+
"Frustration: 2,
|
| 29 |
+
"Neutral": 3,
|
| 30 |
+
"Relief": 4,
|
| 31 |
+
"Disappointment: 5,
|
| 32 |
+
"Confusion": 6,
|
| 33 |
+
"Stress": 7,
|
| 34 |
+
"Negative Strong Emotion": 8
|
| 35 |
},
|
| 36 |
"layer_norm_eps": 1e-12,
|
| 37 |
"max_position_embeddings": 512,
|