Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use jjderz/toxic-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjderz/toxic-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jjderz/toxic-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jjderz/toxic-classifier") model = AutoModelForSequenceClassification.from_pretrained("jjderz/toxic-classifier") - Notebooks
- Google Colab
- Kaggle
Updated Epochs
Browse files
README.md
CHANGED
|
@@ -15,7 +15,7 @@ probably proofread and complete it, then remove this comment. -->
|
|
| 15 |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
- Train Loss: 0.1543
|
| 18 |
-
- Epoch:
|
| 19 |
|
| 20 |
## Model description
|
| 21 |
|
|
@@ -41,7 +41,7 @@ The following hyperparameters were used during training:
|
|
| 41 |
|
| 42 |
| Train Loss | Epoch |
|
| 43 |
|:----------:|:-----:|
|
| 44 |
-
| 0.1543 |
|
| 45 |
|
| 46 |
|
| 47 |
### Framework versions
|
|
|
|
| 15 |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
- Train Loss: 0.1543
|
| 18 |
+
- Epoch: 1
|
| 19 |
|
| 20 |
## Model description
|
| 21 |
|
|
|
|
| 41 |
|
| 42 |
| Train Loss | Epoch |
|
| 43 |
|:----------:|:-----:|
|
| 44 |
+
| 0.1543 | 1 |
|
| 45 |
|
| 46 |
|
| 47 |
### Framework versions
|