Add SetFit model

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: akhooli/Arabic-SBERT-100K
+base_model: akhooli/sbert_ar_nli_500k
 library_name: setfit
 metrics:
 - accuracy
@@ -35,7 +35,7 @@ widget:
     فالفندق
 inference: true
 model-index:
-- name: SetFit with akhooli/Arabic-SBERT-100K
+- name: SetFit with akhooli/sbert_ar_nli_500k
   results:
   - task:
       type: text-classification
@@ -46,13 +46,13 @@ model-index:
       split: test
     metrics:
     - type: accuracy
-      value: 0.49646464646464644
+      value: 0.46636363636363637
       name: Accuracy
 ---
 
-# SetFit with akhooli/Arabic-SBERT-100K
+# SetFit with akhooli/sbert_ar_nli_500k
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/Arabic-SBERT-100K](https://huggingface.co/akhooli/Arabic-SBERT-100K) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k](https://huggingface.co/akhooli/sbert_ar_nli_500k) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -63,7 +63,7 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [akhooli/Arabic-SBERT-100K](https://huggingface.co/akhooli/Arabic-SBERT-100K)
+- **Sentence Transformer body:** [akhooli/sbert_ar_nli_500k](https://huggingface.co/akhooli/sbert_ar_nli_500k)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
 - **Maximum Sequence Length:** 512 tokens
 - **Number of Classes:** 3 classes
@@ -89,7 +89,7 @@ The model has been trained using an efficient few-shot learning technique that i
 ### Metrics
 | Label   | Accuracy |
 |:--------|:---------|
-| **all** | 0.4965   |
+| **all** | 0.4664   |
 
 ## Uses
 
@@ -170,11 +170,11 @@ preds = model("مكان راحه البال . المكان نظيف جدا وم
 ### Training Results
 | Epoch  | Step | Training Loss | Validation Loss |
 |:------:|:----:|:-------------:|:---------------:|
-| 0.0833 | 1    | 0.1967        | -               |
-| 1.0    | 12   | -             | 0.2518          |
-| 2.0    | 24   | -             | 0.2900          |
-| 3.0    | 36   | -             | 0.2936          |
-| 4.0    | 48   | -             | 0.2964          |
+| 0.0833 | 1    | 0.2047        | -               |
+| 1.0    | 12   | -             | 0.2570          |
+| 2.0    | 24   | -             | 0.2721          |
+| 3.0    | 36   | -             | 0.2744          |
+| 4.0    | 48   | -             | 0.2741          |
 
 ### Framework Versions
 - Python: 3.10.14

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "akhooli/Arabic-SBERT-100K",
+  "_name_or_path": "akhooli/sbert_ar_nli_500k",
   "architectures": [
     "BertModel"
   ],

config_setfit.json

@@ -1,8 +1,8 @@
 {
+  "normalize_embeddings": false,
   "labels": [
     "Negative",
     "Positive",
     "Mixed"
-  ],
-  "normalize_embeddings": false
+  ]
 }

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:371918891d5e1705e6e5bdf494f8d81a1f0cc8c8ff58cda6a03b30fd6b701668
 size 540795752

model_head.pkl

@@ -1,3 +1,3 @@
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-oid sha256:0cceeffcadaee046eb44ecebfb588a2896dbe29f1121815b41df9085b1911ff2
+oid sha256:1419f33885c2a0aca451451040cd7f8c3fb8525745151bf49e5db609c8871107
 size 19375