Instructions to use EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022") model = AutoModelForMaskedLM.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "your_path/model", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForMaskedLM" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dim": 3072, | |
| "hidden_dropout_prob": 0.1, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.24.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28996 | |
| } | |