Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use HarshaB1983/assign5autotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HarshaB1983/assign5autotrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HarshaB1983/assign5autotrain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HarshaB1983/assign5autotrain") model = AutoModelForSequenceClassification.from_pretrained("HarshaB1983/assign5autotrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6388372182846069
f1_macro: 0.7533020080884588
f1_micro: 0.7533333333333333
f1_weighted: 0.7533020080884587
precision_macro: 0.7551310982162045
precision_micro: 0.7533333333333333
precision_weighted: 0.7551310982162046
recall_macro: 0.7533333333333333
recall_micro: 0.7533333333333333
recall_weighted: 0.7533333333333333
accuracy: 0.7533333333333333
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Model tree for HarshaB1983/assign5autotrain
Base model
google-bert/bert-base-uncased