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
| { | |
| "_name_or_path": "bigscience/bloom-560m", | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "BloomForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_softmax_in_fp32": true, | |
| "bias_dropout_fusion": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "Negative", | |
| "1": "Positive", | |
| "2": "Neutral" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "Negative": 0, | |
| "Positive": 1, | |
| "Neutral": 2 | |
| }, | |
| "layer_norm_epsilon": 1e-05, | |
| "masked_softmax_fusion": true, | |
| "model_type": "bloom", | |
| "n_head": 16, | |
| "n_inner": null, | |
| "n_layer": 24, | |
| "offset_alibi": 100, | |
| "pad_token_id": 3, | |
| "pretraining_tp": 1, | |
| "problem_type": "single_label_classification", | |
| "seq_length": 2048, | |
| "skip_bias_add": true, | |
| "skip_bias_add_qkv": false, | |
| "slow_but_exact": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.22.1", | |
| "unk_token_id": 0, | |
| "use_cache": true, | |
| "vocab_size": 250880 | |
| } |