Text Classification
Transformers
ONNX
Safetensors
English
Portuguese
modernbert
feature-extraction
typestrict
decision-model
typed-decisions
encoder
decision-index
cpu
text-embeddings-inference
Instructions to use typestrict/TS-moBERTo-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use typestrict/TS-moBERTo-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="typestrict/TS-moBERTo-150M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("typestrict/TS-moBERTo-150M") model = AutoModel.from_pretrained("typestrict/TS-moBERTo-150M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from typestrict/TS-moBERTo-150M: direct link, hf CLI and curl.
- Browser
- Download file 2.12 kB
-
https://huggingface.co/typestrict/TS-moBERTo-150M/resolve/main/config.json
- Command line
-
hf download hf://typestrict/TS-moBERTo-150M/config.json
-
curl -L -o config.json https://huggingface.co/typestrict/TS-moBERTo-150M/resolve/main/config.json
2.12 kB
| { | |
| "architectures": [ | |
| "ModernBertModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50281, | |
| "classifier_activation": "silu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 50282, | |
| "global_attn_every_n_layers": 3, | |
| "global_rope_theta": 160000.0, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "layer_norm_eps": 1e-05, | |
| "local_attention": 128, | |
| "local_rope_theta": 10000.0, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 50283, | |
| "position_embedding_type": "absolute", | |
| "sep_token_id": 50282, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.0", | |
| "vocab_size": 50369, | |
| "dinah": { | |
| "opt_token": "[OPT]", | |
| "opt_token_id": 50368, | |
| "question_types": { | |
| "noul": 0, | |
| "choice": 1, | |
| "score": 2 | |
| }, | |
| "max_length": 8192, | |
| "pad_multiple": 64, | |
| "calibration": { | |
| "temperature": [ | |
| [ | |
| 1.3, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| [ | |
| 1.0, | |
| 1.0, | |
| 1.4, | |
| 1.35, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| [ | |
| 1.0, | |
| 1.0, | |
| 1.3, | |
| 1.3, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ] | |
| ], | |
| "platt": [ | |
| 0.7, | |
| -0.1 | |
| ], | |
| "note": "temperature[question_type][min(max(k,2),9)-2] divides the logits (fit on soft-label NLL); platt maps the confidence logit: sigmoid(a*z+b). Fit on half of the typed-decisions test sets (even indices); the other half is held out." | |
| } | |
| } | |
| } |