update model card README.md

README.md

@@ -10,8 +10,6 @@ metrics:
 model-index:
 - name: kg_model
   results: []
-datasets:
-- vishnun/NLP-KnowledgeGraph
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,17 +17,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 # kg_model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the custom built dataset from publicaly available sentences dataset in Kaggle dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2587
-- Precision: 0.8356
-- Recall: 0.8057
-- F1: 0.8204
-- Accuracy: 0.9170
+- Loss: 0.3039
+- Precision: 0.7629
+- Recall: 0.7025
+- F1: 0.7315
+- Accuracy: 0.8965
 
 ## Model description
 
-Finetuned model for knowledge graph creation in NLP. The dataset(~20k) was created by creating KG using the spaCy library. The original dataset is available in [kaggle](https://www.kaggle.com/datasets/mfekadu/sentences)
+More information needed
 
 ## Intended uses & limitations
 
@@ -56,15 +54,15 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.4931        | 1.0   | 957  | 0.3031          | 0.7872    | 0.7592 | 0.7729 | 0.8935   |
-| 0.2693        | 2.0   | 1914 | 0.2645          | 0.8345    | 0.7868 | 0.8100 | 0.9110   |
-| 0.2142        | 3.0   | 2871 | 0.2602          | 0.8330    | 0.7980 | 0.8152 | 0.9152   |
-| 0.1894        | 4.0   | 3828 | 0.2587          | 0.8356    | 0.8057 | 0.8204 | 0.9170   |
+| 0.3736        | 1.0   | 1063 | 0.3379          | 0.7542    | 0.6217 | 0.6816 | 0.8813   |
+| 0.3078        | 2.0   | 2126 | 0.3075          | 0.7728    | 0.6678 | 0.7164 | 0.8929   |
+| 0.267         | 3.0   | 3189 | 0.3017          | 0.7597    | 0.6999 | 0.7285 | 0.8954   |
+| 0.2455        | 4.0   | 4252 | 0.3039          | 0.7629    | 0.7025 | 0.7315 | 0.8965   |
 
 
 ### Framework versions
 
-- Transformers 4.26.0
+- Transformers 4.27.3
 - Pytorch 1.13.1+cu116
-- Datasets 2.9.0
-- Tokenizers 0.13.2
+- Datasets 2.10.1
+- Tokenizers 0.13.2