Token Classification
Scikit-learn
PyTorch
TensorBoard
Safetensors
Transformers
English
distilbert
ner
mlflow
openchs
Eval Results (legacy)
Instructions to use openchs/ner_distillbert_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use openchs/ner_distillbert_v1 with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("openchs/ner_distillbert_v1", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Transformers
How to use openchs/ner_distillbert_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="openchs/ner_distillbert_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("openchs/ner_distillbert_v1") model = AutoModelForTokenClassification.from_pretrained("openchs/ner_distillbert_v1") - Notebooks
- Google Colab
- Kaggle
updated v2 model metrics
Browse files- metrics_v2.json +11 -0
metrics_v2.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"eval_loss": 0.04738905280828476,
|
| 3 |
+
"eval_accuracy": 0.9865758384146341,
|
| 4 |
+
"eval_f1": 0.9853804851008087,
|
| 5 |
+
"eval_recall": 0.9865758384146341,
|
| 6 |
+
"eval_precision": 0.9857633330258246,
|
| 7 |
+
"eval_runtime": 1.6753,
|
| 8 |
+
"eval_samples_per_second": 122.365,
|
| 9 |
+
"eval_steps_per_second": 15.519,
|
| 10 |
+
"epoch": 5.0
|
| 11 |
+
}
|