Update README.md

README.md

@@ -45,19 +45,14 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
 - **spaCy Model:** en_core_web_sm
-- **SetFitABSA Aspect Model:** [/scratch/project_2006600/fin_experiment/models/setfit-finance-aspect](https://huggingface.co//scratch/project_2006600/fin_experiment/models/setfit-finance-aspect)
-- **SetFitABSA Polarity Model:** [/scratch/project_2006600/fin_experiment/models/setfit-finance-polarity](https://huggingface.co//scratch/project_2006600/fin_experiment/models/setfit-finance-polarity)
+- **SetFitABSA Aspect Model:** [/Askinkaty/setfit-finance-aspect](https://huggingface.co/Askinkaty/setfit-finance-aspect)
+- **SetFitABSA Polarity Model:** [/Askinkaty/setfit-finance-polarity](https://huggingface.co/Askinkaty/setfit-finance-polarity)
 - **Maximum Sequence Length:** 384 tokens
 - **Number of Classes:** 3 classes
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
-### Model Sources
-
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
 
 ### Model Labels
 | Label    | Examples                                                                                                                                                                                                                                                                                                                                                       |
@@ -83,11 +78,11 @@ from setfit import AbsaModel
 
 # Download from the 🤗 Hub
 model = AbsaModel.from_pretrained(
-    "/scratch/project_2006600/fin_experiment/models/setfit-finance-aspect",
-    "/scratch/project_2006600/fin_experiment/models/setfit-finance-polarity",
+    "Askinkaty/setfit-finance-aspect",
+    "Askinkaty/setfit-finance-polarity",
 )
 # Run inference
-preds = model("The food was great, but the venue is just way too busy.")
+preds = model("Banking stocks to see lot of traction: Mitesh Thacker.")
 ```
 
 <!--
@@ -128,11 +123,11 @@ preds = model("The food was great, but the venue is just way too busy.")
 | positive | 703                   |
 
 ### Training Hyperparameters
-- batch_size: (64, 64)
-- num_epochs: (2, 2)
+- batch_size: 64
+- num_epochs: 2
 - max_steps: -1
 - sampling_strategy: oversampling
-- body_learning_rate: (2e-05, 1e-05)
+- body_learning_rate: 1e-05
 - head_learning_rate: 0.01
 - loss: CosineSimilarityLoss
 - distance_metric: cosine_distance
@@ -145,28 +140,6 @@ preds = model("The food was great, but the venue is just way too busy.")
 - eval_max_steps: -1
 - load_best_model_at_end: True
 
-### Training Results
-| Epoch  | Step | Training Loss | Validation Loss |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0000 | 1    | 0.2969        | -               |
-| 0.0025 | 50   | 0.3201        | -               |
-| 0.0049 | 100  | 0.2933        | 0.2839          |
-| 0.0074 | 150  | 0.262         | -               |
-| 0.0098 | 200  | 0.2523        | 0.2480          |
-| 0.0123 | 250  | 0.2403        | -               |
-| 0.0147 | 300  | 0.2185        | 0.2199          |
-| 0.0172 | 350  | 0.1983        | -               |
-| 0.0196 | 400  | 0.1874        | 0.2003          |
-| 0.0221 | 450  | 0.1727        | -               |
-| 0.0245 | 500  | 0.1568        | 0.1882          |
-| 0.0270 | 550  | 0.1386        | -               |
-| 0.0294 | 600  | 0.1181        | 0.1742          |
-| 0.0319 | 650  | 0.1023        | -               |
-| 0.0343 | 700  | 0.0877        | 0.1766          |
-| 0.0368 | 750  | 0.0717        | -               |
-| 0.0392 | 800  | 0.0555        | 0.1854          |
-| 0.0417 | 850  | 0.0447        | -               |
-| 0.0441 | 900  | 0.0343        | 0.1841          |
 
 ### Framework Versions
 - Python: 3.11.11