Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use lomov/strategydisofmaterialimpactsv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lomov/strategydisofmaterialimpactsv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lomov/strategydisofmaterialimpactsv1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lomov/strategydisofmaterialimpactsv1") model = AutoModelForSequenceClassification.from_pretrained("lomov/strategydisofmaterialimpactsv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lomov/strategydisofmaterialimpactsv1: direct link, hf CLI and curl.
- Browser
- Download file 600 Bytes
-
https://huggingface.co/lomov/strategydisofmaterialimpactsv1/resolve/main/README.md
- Command line
-
hf download hf://lomov/strategydisofmaterialimpactsv1/README.md
-
curl -L -o README.md https://huggingface.co/lomov/strategydisofmaterialimpactsv1/resolve/main/README.md
600 Bytes
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- strategydisofmaterialimpactsv1/autotrain-data
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.4904100298881531
f1_macro: 0.851601435352396
f1_micro: 0.8658536585365854
f1_weighted: 0.8538194199208925
precision_macro: 0.8594329005283454
precision_micro: 0.8658536585365854
precision_weighted: 0.8606490578892111
recall_macro: 0.862797619047619
recall_micro: 0.8658536585365854
recall_weighted: 0.8658536585365854
accuracy: 0.8658536585365854