Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use clincolnoz/bert-base-uncased-edos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clincolnoz/bert-base-uncased-edos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clincolnoz/bert-base-uncased-edos")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clincolnoz/bert-base-uncased-edos") model = AutoModelForSequenceClassification.from_pretrained("clincolnoz/bert-base-uncased-edos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from clincolnoz/bert-base-uncased-edos: direct link, hf CLI and curl.
- Browser
- Download file 864 Bytes
-
https://huggingface.co/clincolnoz/bert-base-uncased-edos/resolve/main/eval_results.json
- Command line
-
hf download hf://clincolnoz/bert-base-uncased-edos/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/clincolnoz/bert-base-uncased-edos/resolve/main/eval_results.json
864 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.87925, | |
| "eval_classification_report": " precision recall f1-score support\n0 0.908042 0.935314 0.921476 3030.00000\n1 0.777019 0.704124 0.738778 970.00000\naccuracy 0.879250 0.879250 0.879250 0.87925\nmacro avg 0.842531 0.819719 0.830127 4000.00000\nweighted avg 0.876269 0.879250 0.877172 4000.00000", | |
| "eval_confusion_matrix": "[[2834 196]\n [ 287 683]]", | |
| "eval_confusion_matrix_norm": "[[0.93531353 0.06468647]\n [0.29587629 0.70412371]]", | |
| "eval_f1": 0.7387777176852353, | |
| "eval_f1_macro": 0.8301269502098749, | |
| "eval_f1_weighted": 0.8771718049600645, | |
| "eval_loss": 0.4823172092437744, | |
| "eval_runtime": 9.6229, | |
| "eval_samples": 4000, | |
| "eval_samples_per_second": 415.677, | |
| "eval_steps_per_second": 25.98 | |
| } |