Instructions to use kurtpayne/skillscan-deberta-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kurtpayne/skillscan-deberta-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base") model = PeftModel.from_pretrained(base_model, "kurtpayne/skillscan-deberta-adapter") - Transformers
How to use kurtpayne/skillscan-deberta-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kurtpayne/skillscan-deberta-adapter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kurtpayne/skillscan-deberta-adapter") model = AutoModelForSequenceClassification.from_pretrained("kurtpayne/skillscan-deberta-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +26 -36
- model.safetensors +3 -0
- training_args.bin +1 -1
config.json
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{
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"architectures": [
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"
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],
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"
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"attention_dropout": 0.0,
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"bos_token_id": 50281,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.0,
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"classifier_pooling": "mean",
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"cls_token_id": 50281,
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"decoder_bias": true,
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"deterministic_flash_attn": false,
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"dtype": "float32",
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"
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"
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"global_attn_every_n_layers": 3,
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"global_rope_theta": 160000.0,
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0",
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"6": "LABEL_6",
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"7": "LABEL_7"
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},
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size":
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_6": 6,
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"LABEL_7": 7
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},
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"layer_norm_eps": 1e-
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"model_type": "modernbert",
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"norm_bias": false,
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"norm_eps": 1e-05,
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"num_attention_heads": 12,
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"num_hidden_layers":
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"pad_token_id":
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"problem_type": "multi_label_classification",
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"transformers_version": "4.57.6",
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}
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{
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"architectures": [
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"DebertaV2ForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0",
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"6": "LABEL_6",
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"7": "LABEL_7"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_6": 6,
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"LABEL_7": 7
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},
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"layer_norm_eps": 1e-07,
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"legacy": true,
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"max_position_embeddings": 512,
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"max_relative_positions": -1,
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"model_type": "deberta-v2",
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"norm_rel_ebd": "layer_norm",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_dropout": 0,
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"pooler_hidden_act": "gelu",
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"pooler_hidden_size": 768,
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"pos_att_type": [
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"p2c",
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"c2p"
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],
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"position_biased_input": false,
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"position_buckets": 256,
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"problem_type": "multi_label_classification",
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"relative_attention": true,
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"share_att_key": true,
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"transformers_version": "4.57.6",
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"type_vocab_size": 0,
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"vocab_size": 128100
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:783e5e16e9e9c72376d564186ffcc553bbb9772472bb22967c7e1273355ad734
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size 737737736
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 5905
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version https://git-lfs.github.com/spec/v1
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oid sha256:6998112520dce45b227e3d2926678830f08fcc68c951bcbbc24fd0bb5987f3f0
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size 5905
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