Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BaxterAI/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaxterAI/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- pytorch_model.bin +1 -1
- runs/May23_09-38-41_2d362c5ccea5/events.out.tfevents.1653298727.2d362c5ccea5.75.0 +2 -2
- runs/May23_09-40-55_2d362c5ccea5/1653298863.3320386/events.out.tfevents.1653298863.2d362c5ccea5.75.3 +3 -0
- runs/May23_09-40-55_2d362c5ccea5/events.out.tfevents.1653298863.2d362c5ccea5.75.2 +3 -0
- training_args.bin +1 -1
pytorch_model.bin
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