Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use agvidit1/distilbert-hate_speech18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agvidit1/distilbert-hate_speech18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agvidit1/distilbert-hate_speech18")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("agvidit1/distilbert-hate_speech18") model = AutoModelForSequenceClassification.from_pretrained("agvidit1/distilbert-hate_speech18") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files- logs/events.out.tfevents.1702154602.tron63.umiacs.umd.edu +2 -2
- model.safetensors +1 -1
- run-0/checkpoint-548/config.json +37 -0
- run-0/checkpoint-548/model.safetensors +3 -0
- run-0/checkpoint-548/optimizer.pt +3 -0
- run-0/checkpoint-548/rng_state.pth +3 -0
- run-0/checkpoint-548/scheduler.pt +3 -0
- run-0/checkpoint-548/special_tokens_map.json +37 -0
- run-0/checkpoint-548/tokenizer_config.json +57 -0
- run-0/checkpoint-548/trainer_state.json +56 -0
- run-0/checkpoint-548/training_args.bin +3 -0
- run-0/checkpoint-548/vocab.txt +0 -0
logs/events.out.tfevents.1702154602.tron63.umiacs.umd.edu
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model.safetensors
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run-0/checkpoint-548/config.json
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{
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"_name_or_path": "agvidit1/DistilledBert_HateSpeech_pretrain",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "noHate",
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"1": "hate",
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"2": "idk/skip",
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"3": "relation"
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},
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"initializer_range": 0.02,
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"label2id": {
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"hate": "1",
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"idk/skip": "2",
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"noHate": "0",
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"relation": "3"
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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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": "4.35.2",
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"vocab_size": 30522
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}
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run-0/checkpoint-548/model.safetensors
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run-0/checkpoint-548/optimizer.pt
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run-0/checkpoint-548/rng_state.pth
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run-0/checkpoint-548/scheduler.pt
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size 1064
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run-0/checkpoint-548/special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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"mask_token": {
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"lstrip": false,
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"normalized": false,
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"single_word": false
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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run-0/checkpoint-548/tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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"special": true
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"special": true
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"single_word": false,
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| 33 |
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"special": true
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| 34 |
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},
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| 35 |
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"103": {
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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"single_word": false,
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| 41 |
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"special": true
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| 42 |
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}
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},
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| 44 |
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"clean_up_tokenization_spaces": true,
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| 45 |
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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| 50 |
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"never_split": null,
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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": "DistilBertTokenizer",
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| 56 |
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"unk_token": "[UNK]"
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}
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run-0/checkpoint-548/trainer_state.json
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{
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"best_metric": 0.8893967093235832,
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"best_model_checkpoint": "distilbert-hate_speech18/run-0/checkpoint-548",
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"epoch": 2.0,
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| 5 |
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"eval_steps": 500,
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"global_step": 548,
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"is_hyper_param_search": true,
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"is_world_process_zero": true,
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"log_history": [
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{
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{
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"epoch": 1.0,
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},
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{
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"learning_rate": 6.160296611279106e-06,
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"step": 548
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},
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{
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"eval_samples_per_second": 483.316,
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"step": 548
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}
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],
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"logging_steps": 500,
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"max_steps": 822,
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"num_train_epochs": 3,
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"save_steps": 500,
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"per_device_train_batch_size": 32,
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"temperature": 15
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}
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}
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run-0/checkpoint-548/training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 4664
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run-0/checkpoint-548/vocab.txt
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