Instructions to use HiAmNear/cafebert-ViFE-task-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiAmNear/cafebert-ViFE-task-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HiAmNear/cafebert-ViFE-task-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiAmNear/cafebert-ViFE-task-1") model = AutoModelForSequenceClassification.from_pretrained("HiAmNear/cafebert-ViFE-task-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 8, checkpoint
Browse files
last-checkpoint/model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"best_metric": 0.
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"best_model_checkpoint": "../../modelParams/TrainingArguments_output/checkpoint-
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"is_world_process_zero": true,
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"eval_samples_per_second": 37.596,
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"eval_steps_per_second": 4.714,
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"step": 313
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"logging_steps": 500,
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"attributes": {}
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{
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"best_metric": 0.8650159744408946,
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"best_model_checkpoint": "../../modelParams/TrainingArguments_output/checkpoint-352",
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"epoch": 8.996805111821086,
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"is_world_process_zero": true,
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"eval_samples_per_second": 37.596,
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{
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"eval_loss": 0.396475613117218,
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"eval_runtime": 33.2277,
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"eval_samples_per_second": 37.679,
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"eval_steps_per_second": 4.725,
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"step": 352
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"logging_steps": 500,
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"attributes": {}
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"total_flos": 2.091585948165261e+16,
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"train_batch_size": 8,
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