Add SetFit ABSA model

README.md

@@ -27,13 +27,13 @@ widget:
 pipeline_tag: text-classification
 inference: false
 co2_eq_emissions:
-  emissions: 10.256079923743641
+  emissions: 15.732253126728272
   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: 0.117
+  hours_used: 0.174
   hardware_used: 1 x NVIDIA GeForce RTX 3090
 base_model: BAAI/bge-small-en-v1.5
 model-index:
@@ -48,7 +48,7 @@ model-index:
       split: test
     metrics:
     - type: accuracy
-      value: 0.7467434110875493
+      value: 0.748561042108452
       name: Accuracy
 ---
 
@@ -73,6 +73,7 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **Model Type:** SetFit
 - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** en_core_web_lg
 - **SetFitABSA Aspect Model:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect)
 - **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
@@ -100,7 +101,7 @@ This model was trained within the context of a larger system for ABSA, which loo
 ### Metrics
 | Label   | Accuracy |
 |:--------|:---------|
-| **all** | 0.7467   |
+| **all** | 0.7486   |
 
 ## Uses
 
@@ -178,30 +179,31 @@ preds = model("The food was great, but the venue is just way too busy.")
 - use_amp: True
 - warmup_proportion: 0.1
 - seed: 42
+- eval_max_steps: -1
 - load_best_model_at_end: True
 
 ### Training Results
 | Epoch      | Step    | Training Loss | Validation Loss |
 |:----------:|:-------:|:-------------:|:---------------:|
-| 0.0078     | 1       | 0.2411        | -               |
-| 0.3876     | 50      | 0.2293        | -               |
-| 0.7752     | 100     | 0.185         | 0.1885          |
-| 1.1628     | 150     | 0.0962        | -               |
-| **1.5504** | **200** | **0.0299**    | **0.1782**      |
-| 1.9380     | 250     | 0.0306        | -               |
-| 2.3256     | 300     | 0.0136        | 0.2029          |
-| 2.7132     | 350     | 0.0065        | -               |
-| 3.1008     | 400     | 0.0024        | 0.229           |
-| 3.4884     | 450     | 0.0014        | -               |
-| 3.8760     | 500     | 0.0016        | 0.2434          |
+| 0.0078     | 1       | 0.2397        | -               |
+| 0.3876     | 50      | 0.2252        | -               |
+| 0.7752     | 100     | 0.1896        | 0.1883          |
+| 1.1628     | 150     | 0.0964        | -               |
+| **1.5504** | **200** | **0.0307**    | **0.1792**      |
+| 1.9380     | 250     | 0.0275        | -               |
+| 2.3256     | 300     | 0.0138        | 0.2036          |
+| 2.7132     | 350     | 0.006         | -               |
+| 3.1008     | 400     | 0.0035        | 0.2287          |
+| 3.4884     | 450     | 0.0015        | -               |
+| 3.8760     | 500     | 0.0016        | 0.2397          |
 | 4.2636     | 550     | 0.001         | -               |
-| 4.6512     | 600     | 0.001         | 0.2483          |
+| 4.6512     | 600     | 0.0009        | 0.2477          |
 
 * The bold row denotes the saved checkpoint.
 ### Environmental Impact
 Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
-- **Carbon Emitted**: 0.010 kg of CO2
-- **Hours Used**: 0.117 hours
+- **Carbon Emitted**: 0.016 kg of CO2
+- **Hours Used**: 0.174 hours
 
 ### Training Hardware
 - **On Cloud**: No
@@ -213,6 +215,7 @@ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codec
 - Python: 3.9.16
 - SetFit: 1.0.0.dev0
 - Sentence Transformers: 2.2.2
+- spaCy: 3.7.2
 - Transformers: 4.29.0
 - PyTorch: 1.13.1+cu117
 - Datasets: 2.15.0

config_setfit.json

@@ -1,4 +1,5 @@
 {
+  "spacy_model": "en_core_web_lg",
   "normalize_embeddings": false,
   "labels": null,
   "span_context": 3

model_head.pkl

@@ -1,3 +1,3 @@
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pytorch_model.bin

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