Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use mtyrrell/CPU_Mitigation_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtyrrell/CPU_Mitigation_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtyrrell/CPU_Mitigation_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Mitigation_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Mitigation_Classifier") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -0
config.json
CHANGED
|
@@ -53,6 +53,7 @@
|
|
| 53 |
"num_attention_heads": 12,
|
| 54 |
"num_hidden_layers": 12,
|
| 55 |
"pad_token_id": 1,
|
|
|
|
| 56 |
"relative_attention_num_buckets": 32,
|
| 57 |
"torch_dtype": "float32",
|
| 58 |
"transformers_version": "4.31.0",
|
|
|
|
| 53 |
"num_attention_heads": 12,
|
| 54 |
"num_hidden_layers": 12,
|
| 55 |
"pad_token_id": 1,
|
| 56 |
+
"problem_type": "multi_label_classification",
|
| 57 |
"relative_attention_num_buckets": 32,
|
| 58 |
"torch_dtype": "float32",
|
| 59 |
"transformers_version": "4.31.0",
|