Add SetFit ABSA model

README.md

@@ -1,4 +1,6 @@
 ---
+language: en
+license: apache-2.0
 library_name: setfit
 tags:
 - setfit
@@ -6,6 +8,8 @@ tags:
 - sentence-transformers
 - text-classification
 - generated_from_setfit_trainer
+datasets:
+- tomaarsen/setfit-absa-semeval-restaurants
 metrics:
 - accuracy
 widget:
@@ -20,17 +24,17 @@ widget:
 pipeline_tag: text-classification
 inference: false
 co2_eq_emissions:
-  emissions: 12.403245052695876
+  emissions: 12.371061343498498
   source: codecarbon
   training_type: fine-tuning
   on_cloud: false
   cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
   ram_total_size: 31.777088165283203
-  hours_used: 0.158
+  hours_used: 0.206
   hardware_used: 1 x NVIDIA GeForce RTX 3090
 base_model: BAAI/bge-small-en-v1.5
 model-index:
-- name: SetFit Aspect Model with BAAI/bge-small-en-v1.5
+- name: SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants)
   results:
   - task:
       type: text-classification
@@ -38,16 +42,16 @@ model-index:
     dataset:
       name: SemEval 2014 Task 4 (Restaurants)
       type: tomaarsen/setfit-absa-semeval-restaurants
-      split: train[384:]
+      split: test
     metrics:
     - type: accuracy
       value: 0.7871243108660857
       name: Accuracy
 ---
 
-# SetFit Aspect Model with BAAI/bge-small-en-v1.5
+# SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants)
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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. In particular, this model is in charge of filtering aspect span candidates.
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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. In particular, this model is in charge of filtering aspect span candidates.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -70,9 +74,9 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **SetFitABSA Polarity Model:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity)
 - **Maximum Sequence Length:** 512 tokens
 - **Number of Classes:** 2 classes
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Training Dataset:** [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants)
+- **Language:** en
+- **License:** apache-2.0
 
 ### Model Sources
 
@@ -194,7 +198,7 @@ preds = model("The food was great, but the venue is just way too busy.")
 ### Environmental Impact
 Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
 - **Carbon Emitted**: 0.012 kg of CO2
-- **Hours Used**: 0.158 hours
+- **Hours Used**: 0.206 hours
 
 ### Training Hardware
 - **On Cloud**: No

config_setfit.json

@@ -1,8 +1,8 @@
 {
+  "normalize_embeddings": false,
   "labels": [
     "no aspect",
     "aspect"
   ],
-  "span_context": 0,
-  "normalize_embeddings": false
+  "span_context": 0
 }