Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use EgorSys/students_scores_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EgorSys/students_scores_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EgorSys/students_scores_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EgorSys/students_scores_model") model = AutoModelForSequenceClassification.from_pretrained("EgorSys/students_scores_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +18 -16
- model.safetensors +2 -2
- runs/Dec06_12-03-07_d7de64af26e3/events.out.tfevents.1733486589.d7de64af26e3.23.0 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +3 -2
- training_args.bin +2 -2
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "
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"activation": "gelu",
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"architectures": [
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"5": "LABEL_5"
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},
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"initializer_range": 0.02,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_4": 4,
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"LABEL_5": 5
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},
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"max_position_embeddings": 512,
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"model_type": "
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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}
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"_name_or_path": "bert-base-cased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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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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"1": "LABEL_1",
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"5": "LABEL_5"
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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_4": 4,
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"LABEL_5": 5
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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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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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.47.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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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 sha256:0b9b13ecaea6dbddb702aa136484ea79a5e5b32faaa0bfc760f56cc298526de0
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size 433283072
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runs/Dec06_12-03-07_d7de64af26e3/events.out.tfevents.1733486589.d7de64af26e3.23.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:9cf5dea3a3c25ddbccfced77aa18ae964200a815efb423cc9836c51cb20da81c
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size 5646
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tokenizer.json
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tokenizer_config.json
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case":
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "
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"unk_token": "[UNK]"
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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training_args.bin
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size 5368
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vocab.txt
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