Instructions to use Shresthadev403/ner-bert-ingredients with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shresthadev403/ner-bert-ingredients with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shresthadev403/ner-bert-ingredients")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shresthadev403/ner-bert-ingredients") model = AutoModelForTokenClassification.from_pretrained("Shresthadev403/ner-bert-ingredients", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 0b338d3
End of training
Browse files- README.md +5 -5
- logs/events.out.tfevents.1704794705.1ec128b7e2b3.26.0 +2 -2
- model.safetensors +1 -1
- trainer_state.json +17 -3
README.md
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 1.
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- eval_runtime: 83.
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- eval_samples_per_second: 59.
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- eval_steps_per_second: 0.944
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- epoch:
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- step:
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## Model description
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 1.4814
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- eval_runtime: 83.6915
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- eval_samples_per_second: 59.743
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- eval_steps_per_second: 0.944
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- epoch: 21.23
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- step: 15750
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## Model description
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logs/events.out.tfevents.1704794705.1ec128b7e2b3.26.0
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model.safetensors
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trainer_state.json
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{
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"best_metric": 1.3803095817565918,
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"best_model_checkpoint": "ner-bert-ingredients/checkpoint-12750",
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"epoch":
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"eval_steps": 750,
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"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 59.726,
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"eval_steps_per_second": 0.944,
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"step": 15000
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}
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"logging_steps": 750,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 100,
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"save_steps": 750,
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"train_batch_size": 16,
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"trial_params": null
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{
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"best_metric": 1.3803095817565918,
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"best_model_checkpoint": "ner-bert-ingredients/checkpoint-12750",
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"epoch": 21.22532839339845,
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"eval_steps": 750,
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"global_step": 15750,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 59.726,
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"eval_steps_per_second": 0.944,
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"step": 15000
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},
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{
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"epoch": 21.23,
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"learning_rate": 4.84845013477089e-05,
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"loss": 1.002,
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"step": 15750
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},
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{
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"epoch": 21.23,
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"eval_loss": 1.481406331062317,
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"eval_runtime": 83.6915,
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"eval_samples_per_second": 59.743,
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"eval_steps_per_second": 0.944,
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"step": 15750
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}
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"logging_steps": 750,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 100,
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"save_steps": 750,
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"total_flos": 6.816569670767002e+17,
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"train_batch_size": 16,
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"trial_name": null,
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"trial_params": null
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