Instructions to use marlex7/trash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marlex7/trash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marlex7/trash")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marlex7/trash") model = AutoModelForSequenceClassification.from_pretrained("marlex7/trash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
config.json
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{
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"_name_or_path": "google/bert_uncased_L-
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"architectures": [
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"BertForSequenceClassification"
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],
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 8,
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"num_hidden_layers":
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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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{
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"_name_or_path": "google/bert_uncased_L-4_H-512_A-8",
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"architectures": [
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"BertForSequenceClassification"
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],
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 8,
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"num_hidden_layers": 4,
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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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logs/events.out.tfevents.1701649593.da95596949d7.194.4
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logs/events.out.tfevents.1701653363.da95596949d7.194.6
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model.safetensors
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size 115067048
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special_tokens_map.json
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"cls_token":
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}
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"pad_token": {
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"single_word": false
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"sep_token": {
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"unk_token": {
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"content": "[UNK]",
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