Instructions to use l3cube-pune/MarathiSentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/MarathiSentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/MarathiSentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/MarathiSentiment") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/MarathiSentiment") - Notebooks
- Google Colab
- Kaggle
Commit ·
6cf0bc0
1
Parent(s): 847f47c
Changing the config labels
Browse files- config.json +3 -3
config.json
CHANGED
|
@@ -22,9 +22,9 @@
|
|
| 22 |
"inner_group_num": 1,
|
| 23 |
"intermediate_size": 3072,
|
| 24 |
"label2id": {
|
| 25 |
-
"
|
| 26 |
-
"
|
| 27 |
-
"
|
| 28 |
},
|
| 29 |
"layer_norm_eps": 1e-12,
|
| 30 |
"max_position_embeddings": 512,
|
|
|
|
| 22 |
"inner_group_num": 1,
|
| 23 |
"intermediate_size": 3072,
|
| 24 |
"label2id": {
|
| 25 |
+
"Negative": 0,
|
| 26 |
+
"Neutral": 1,
|
| 27 |
+
"Positive": 2
|
| 28 |
},
|
| 29 |
"layer_norm_eps": 1e-12,
|
| 30 |
"max_position_embeddings": 512,
|