Instructions to use Adapting/comfort_congratulations_neutral-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adapting/comfort_congratulations_neutral-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adapting/comfort_congratulations_neutral-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adapting/comfort_congratulations_neutral-classifier") model = AutoModelForSequenceClassification.from_pretrained("Adapting/comfort_congratulations_neutral-classifier") - Notebooks
- Google Colab
- Kaggle
v2
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
CHANGED
|
@@ -30,6 +30,6 @@
|
|
| 30 |
"sinusoidal_pos_embds": false,
|
| 31 |
"tie_weights_": true,
|
| 32 |
"torch_dtype": "float32",
|
| 33 |
-
"transformers_version": "4.
|
| 34 |
"vocab_size": 30522
|
| 35 |
}
|
|
|
|
| 30 |
"sinusoidal_pos_embds": false,
|
| 31 |
"tie_weights_": true,
|
| 32 |
"torch_dtype": "float32",
|
| 33 |
+
"transformers_version": "4.20.0",
|
| 34 |
"vocab_size": 30522
|
| 35 |
}
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 267857393
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2ea120f4d4ee6a0fc2784a40009b7762ad43286c44a1a9ab2c87ebff92c7f8df
|
| 3 |
size 267857393
|