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 ·
cb2760c
1
Parent(s): b845e1a
updating readme file
Browse files
README.md
CHANGED
|
@@ -12,6 +12,7 @@ widget:
|
|
| 12 |
|
| 13 |
## MarathiSentiment
|
| 14 |
|
| 15 |
-
MarathiSentiment is
|
|
|
|
| 16 |
|
| 17 |
More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408)
|
|
|
|
| 12 |
|
| 13 |
## MarathiSentiment
|
| 14 |
|
| 15 |
+
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
|
| 16 |
+
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
|
| 17 |
|
| 18 |
More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408)
|