Update README.md
Browse files
README.md
CHANGED
|
@@ -1,12 +1,12 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
-
base_model: bert-
|
| 4 |
tags:
|
| 5 |
- adult text classification
|
| 6 |
metrics:
|
| 7 |
- accuracy
|
| 8 |
model-index:
|
| 9 |
-
- name: bert-
|
| 10 |
results: []
|
| 11 |
datasets:
|
| 12 |
- valurank/Adult-content-dataset
|
|
@@ -14,8 +14,8 @@ language:
|
|
| 14 |
- en
|
| 15 |
---
|
| 16 |
|
| 17 |
-
# bert-
|
| 18 |
-
This model is a fine-tuned version of [bert-
|
| 19 |
It achieves the following results on the evaluation set:
|
| 20 |
- Loss: 0.1257
|
| 21 |
- Accuracy: 0.9824
|
|
@@ -68,4 +68,4 @@ The following hyperparameters were used during training:
|
|
| 68 |
- Datasets 2.16.1
|
| 69 |
- Tokenizers 0.15.0
|
| 70 |
|
| 71 |
-
This model card provides an overview of the model's architecture, training procedure, and performance metrics. It serves as a reference for users interested in utilizing or further understanding the capabilities and limitations of the bert-
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
+
base_model: bert-large-uncased
|
| 4 |
tags:
|
| 5 |
- adult text classification
|
| 6 |
metrics:
|
| 7 |
- accuracy
|
| 8 |
model-index:
|
| 9 |
+
- name: bert-large-uncased-Adult-Text-Classifier
|
| 10 |
results: []
|
| 11 |
datasets:
|
| 12 |
- valurank/Adult-content-dataset
|
|
|
|
| 14 |
- en
|
| 15 |
---
|
| 16 |
|
| 17 |
+
# bert-large-uncased-Adult-Text-Classifier
|
| 18 |
+
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the [valurank/Adult-content-dataset](https://huggingface.co/datasets/valurank/Adult-content-dataset). It has been trained to classify text into categories related to adult content.
|
| 19 |
It achieves the following results on the evaluation set:
|
| 20 |
- Loss: 0.1257
|
| 21 |
- Accuracy: 0.9824
|
|
|
|
| 68 |
- Datasets 2.16.1
|
| 69 |
- Tokenizers 0.15.0
|
| 70 |
|
| 71 |
+
This model card provides an overview of the model's architecture, training procedure, and performance metrics. It serves as a reference for users interested in utilizing or further understanding the capabilities and limitations of the bert-large-uncased-Adult-Text-Classifier model.
|