Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use lomov/strategytransitionplanv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lomov/strategytransitionplanv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lomov/strategytransitionplanv1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lomov/strategytransitionplanv1") model = AutoModelForSequenceClassification.from_pretrained("lomov/strategytransitionplanv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lomov/strategytransitionplanv1: direct link, hf CLI and curl.
- Browser
- Download file 597 Bytes
-
https://huggingface.co/lomov/strategytransitionplanv1/resolve/main/README.md
- Command line
-
hf download hf://lomov/strategytransitionplanv1/README.md
-
curl -L -o README.md https://huggingface.co/lomov/strategytransitionplanv1/resolve/main/README.md
597 Bytes
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- strategytransitionplanv1/autotrain-data
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.12461505830287933
f1_macro: 0.9837010534684953
f1_micro: 0.9838709677419355
f1_weighted: 0.9838517321638102
precision_macro: 0.9848484848484849
precision_micro: 0.9838709677419355
precision_weighted: 0.9846041055718475
recall_macro: 0.9833333333333334
recall_micro: 0.9838709677419355
recall_weighted: 0.9838709677419355
accuracy: 0.9838709677419355