Instructions to use ScriptEdgeAI/MarathiSentiment-Bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ScriptEdgeAI/MarathiSentiment-Bloom-560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ScriptEdgeAI/MarathiSentiment-Bloom-560m")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ScriptEdgeAI/MarathiSentiment-Bloom-560m") model = AutoModelForSequenceClassification.from_pretrained("ScriptEdgeAI/MarathiSentiment-Bloom-560m") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - mr | |
| tags: | |
| - mr | |
| - Sentiment-Analysis | |
| license: cc-by-nc-4.0 | |
| widget: | |
| - text: "मला तुम्ही आवडता. मी तुझ्यावर प्रेम करतो." | |
| # Marathi-Bloom-560m is a Bloom fine-tuned model trained by ScriptEdge on MahaNLP tweets dataset from L3Cube-MahaNLP. | |
| ## Worked on by: | |
| Trained by: | |
| - Venkatesh Soni. | |
| Assistance: | |
| - Rayansh Srivastava. | |
| Supervision: | |
| - Akshay Ugale, Madhukar Alhat. | |
| ## Usage - | |
| - It is intended for non-commercial usages. | |
| ## Model best metrics | |
| | *Model* | *Data* | *Accuracy* | | |
| |---------------------------------------------------|---------------------|-------------------| | |
| | bigscience/bloom-560m | Validation | 34.7 | | |
| | bigscience/bloom-560m | Test | **34.8** | | |
| | ScriptEdgeAI/MarathiSentiment-Bloom-560m | Validation | 76.0 | | |
| | ScriptEdgeAI/MarathiSentiment-Bloom-560m | Test | **77.0** | | |
| Citation to L3CubePune by the dataset usage. | |
| ``` | |
| @article {joshi2022l3cube, | |
| title= {L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library}, | |
| author= {Joshi, Raviraj}, | |
| journal= {arXiv preprint arXiv:2205.14728}, | |
| year= {2022} | |
| } | |
| ``` |