Instructions to use nikraf/directionality_probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikraf/directionality_probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nikraf/directionality_probe", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikraf/directionality_probe", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_token_ids": false, | |
| "architectures": [ | |
| "PackagedProbeModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "packaged_probe_model.PackagedProbeConfig", | |
| "AutoModel": "packaged_probe_model.PackagedProbeModel" | |
| }, | |
| "base_model_name": "ESMC-600", | |
| "dtype": "float32", | |
| "matrix_embed": true, | |
| "model_type": "packaged_probe", | |
| "pooling_types": [ | |
| "mean", | |
| "var" | |
| ], | |
| "ppi": true, | |
| "probe_config": { | |
| "_name_or_path": "", | |
| "add_cross_attention": false, | |
| "add_token_ids": false, | |
| "architectures": [ | |
| "TransformerForSequenceClassification" | |
| ], | |
| "bad_words_ids": null, | |
| "begin_suppress_tokens": null, | |
| "bos_token_id": null, | |
| "chunk_size_feed_forward": 0, | |
| "classifier_dropout": 0.2, | |
| "classifier_size": 4096, | |
| "cross_attention_hidden_size": null, | |
| "decoder_start_token_id": null, | |
| "diversity_penalty": 0.0, | |
| "do_sample": false, | |
| "dropout": 0.2, | |
| "dtype": "float32", | |
| "early_stopping": false, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "eos_token_id": null, | |
| "exponential_decay_length_penalty": null, | |
| "finetuning_task": null, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "hidden_size": 512, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "input_size": 1152, | |
| "is_decoder": false, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "length_penalty": 1.0, | |
| "lora": false, | |
| "lora_alpha": 32.0, | |
| "lora_dropout": 0.01, | |
| "lora_r": 8, | |
| "max_length": 20, | |
| "min_length": 0, | |
| "model_type": "probe", | |
| "n_heads": 4, | |
| "n_layers": 1, | |
| "no_repeat_ngram_size": 0, | |
| "num_beam_groups": 1, | |
| "num_beams": 1, | |
| "num_return_sequences": 1, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_scores": false, | |
| "pad_token_id": null, | |
| "pooling_types": [ | |
| "mean", | |
| "cls" | |
| ], | |
| "pre_ln": true, | |
| "prefix": null, | |
| "probe_type": "transformer", | |
| "problem_type": null, | |
| "pruned_heads": {}, | |
| "remove_invalid_values": false, | |
| "repetition_penalty": 1.0, | |
| "return_dict": true, | |
| "return_dict_in_generate": false, | |
| "rotary": true, | |
| "sep_token_id": null, | |
| "sim_type": "dot", | |
| "suppress_tokens": null, | |
| "task_specific_params": null, | |
| "task_type": "singlelabel", | |
| "temperature": 1.0, | |
| "tf_legacy_loss": false, | |
| "tie_encoder_decoder": false, | |
| "tie_word_embeddings": true, | |
| "token_attention": false, | |
| "tokenizer_class": null, | |
| "tokenwise": false, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "torchscript": false, | |
| "transformer_dropout": 0.1, | |
| "transformer_hidden_size": 512, | |
| "transformers_version": "4.57.6", | |
| "typical_p": 1.0, | |
| "use_bfloat16": false, | |
| "use_bias": false | |
| }, | |
| "probe_type": "transformer", | |
| "sep_token_id": 2, | |
| "task_type": "singlelabel", | |
| "tokenwise": false, | |
| "transformers_version": "4.57.6" | |
| } | |