Text Classification
Transformers
PyTorch
Safetensors
English
longformer
Generated from Trainer
Eval Results (legacy)
Instructions to use nbroad/longformer-base-health-fact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/longformer-base-health-fact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbroad/longformer-base-health-fact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/longformer-base-health-fact") model = AutoModelForSequenceClassification.from_pretrained("nbroad/longformer-base-health-fact") - Notebooks
- Google Colab
- Kaggle
id2label keys back to str
Browse files- config.json +4 -4
config.json
CHANGED
|
@@ -29,10 +29,10 @@
|
|
| 29 |
"hidden_dropout_prob": 0.1,
|
| 30 |
"hidden_size": 768,
|
| 31 |
"id2label": {
|
| 32 |
-
0: "false",
|
| 33 |
-
1: "mixture",
|
| 34 |
-
2: "true",
|
| 35 |
-
3: "unproven"
|
| 36 |
},
|
| 37 |
"ignore_attention_mask": false,
|
| 38 |
"initializer_range": 0.02,
|
|
|
|
| 29 |
"hidden_dropout_prob": 0.1,
|
| 30 |
"hidden_size": 768,
|
| 31 |
"id2label": {
|
| 32 |
+
"0": "false",
|
| 33 |
+
"1": "mixture",
|
| 34 |
+
"2": "true",
|
| 35 |
+
"3": "unproven"
|
| 36 |
},
|
| 37 |
"ignore_attention_mask": false,
|
| 38 |
"initializer_range": 0.02,
|