Update README.md

README.md

@@ -210,9 +210,9 @@ for match in matches:
 *Dataset: WiRe57_343-manual-oie*
 | Model                  |   Precision |   Recall |   F1 Score |
 |:-----------------------|------------:|---------:|-----------:|
-| knowledgator/gliner-llama-multitask-1B-v1.0 |    0.914894 | 0.200466 |   0.328872 |
-| knowledgator/gliner-multitask-v0.5  |    0.848485 | 0.140351 |   0.24086  |
-| knowledgator/gliner-multitask-v1.0  |    0.9      | 0.155172 |   0.264706 |
+| knowledgator/gliner-llama-multitask-1B-v1.0 |    0.9047 | 0.2794 |   0.4269 |
+| knowledgator/gliner-multitask-v0.5  |    0.9278 | 0.2779 |   0.4287  |
+| knowledgator/gliner-multitask-v1.0  |    0.8775      | 0.2733 |   0.4168 |
 
 ---
 
@@ -235,7 +235,6 @@ for answer in answers:
 
 ### Performance:
 *Dataset: SQuAD 2.0*
-
 | Model                  |   Precision |   Recall |   F1 Score |
 |:-----------------------|------------:|---------:|-----------:|
 | knowledgator/gliner-llama-multitask-1B-v1.0  |    0.578296 | 0.795821 |   0.669841 |
@@ -264,22 +263,12 @@ labels = ["summary"]
 
 input_ = prompt+text
 
-threshold = 0.5
+threshold = 0.1
 summaries = model.predict_entities(input_, labels, threshold=threshold)
 
 for summary in summaries:
     print(summary["text"], "=>", summary["score"])
 ```
-
-### Performance:
-*Dataset: SQuAD 2.0*
-
-| Model                  |        BLEU |    ROUGE1 |     ROUGE2 |    ROUGEL |   Cosine Similarity |
-|:-----------------------|------------:|----------:|-----------:|----------:|--------------------:|
-| knowledgator/gliner-llama-multitask-1B-v1.0 | 7.9728e-157 | 0.0955005 | 0.00236265 | 0.0738533 |           0.0515591 |
-| knowledgator/gliner-multitask-v0.5  | 1.70326e-06 | 0.0627964 | 0.00203505 | 0.0482932 |           0.0532316 |
-| knowledgator/gliner-multitask-v1.0  | 5.78799e-06 | 0.0878883 | 0.0030312  | 0.0657152 |           0.060342  |
-
 ---
 
 **How to use for text classification:**