Instructions to use omarmomen/sf_ip_babylm_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarmomen/sf_ip_babylm_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="omarmomen/sf_ip_babylm_1", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("omarmomen/sf_ip_babylm_1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "StructFormer_In_ParserModel", | |
| "StructFormer_In_ParserModelForSequenceClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "structformer_in_parser.StructFormer_In_ParserConfig", | |
| "AutoModelForMaskedLM": "structformer_in_parser.StructFormer_In_ParserModel", | |
| "AutoModelForSequenceClassification": "structformer_in_parser.StructFormer_In_ParserModelForSequenceClassification" | |
| }, | |
| "conv_size": 9, | |
| "dropatt": 0.1, | |
| "dropout": 0.1, | |
| "front_layers": 4, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "model_type": "structformer_in_parser", | |
| "n_parser_layers": 3, | |
| "nhead": 8, | |
| "nlayers": 8, | |
| "ntokens": 16000, | |
| "pad": 1, | |
| "pos_emb": true, | |
| "rear_layers": 4, | |
| "relations": [ | |
| "head", | |
| "child" | |
| ], | |
| "relative_bias": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.18.0", | |
| "weight_act": "softmax" | |
| } | |