Keras
Nepali
text-classification
sentiment-analysis
nepali
nepse
stock-market
finance
bilstm
cnn
Eval Results (legacy)
Instructions to use robinGiri2024/nepse-sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use robinGiri2024/nepse-sentiment-analyzer with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://robinGiri2024/nepse-sentiment-analyzer") - Notebooks
- Google Colab
- Kaggle
Download config.json from robinGiri2024/nepse-sentiment-analyzer: direct link, hf CLI and curl.
- Browser
- Download file 491 Bytes
-
https://huggingface.co/robinGiri2024/nepse-sentiment-analyzer/resolve/main/config.json
- Command line
-
hf download hf://robinGiri2024/nepse-sentiment-analyzer/config.json
-
curl -L -o config.json https://huggingface.co/robinGiri2024/nepse-sentiment-analyzer/resolve/main/config.json
491 Bytes
| { | |
| "architectures": [ | |
| "BiLSTM-CNN" | |
| ], | |
| "model_type": "text-classification", | |
| "num_labels": 5, | |
| "id2label": { | |
| "0": "Very Negative", | |
| "1": "Negative", | |
| "2": "Neutral", | |
| "3": "Positive", | |
| "4": "Very Positive" | |
| }, | |
| "label2id": { | |
| "Very Negative": 0, | |
| "Negative": 1, | |
| "Neutral": 2, | |
| "Positive": 3, | |
| "Very Positive": 4 | |
| }, | |
| "max_length": 100, | |
| "vocab_size": 10000, | |
| "embedding_dim": 100, | |
| "lstm_units": 64, | |
| "cnn_filters": 128, | |
| "kernel_size": 3 | |
| } |