Update README.md

README.md

@@ -2,87 +2,70 @@
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
-- sentence-similarity
-
+- Text Classification
+license: gpl-3.0
+language:
+- en
 ---
 
-# {MODEL_NAME}
+# FewShotIssueClassifier-NLBSE23
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a SetFit model using Sentence Transformers to map sentences & paragraphs to a 768 dimensional dense vector space. It be used for tasks like clustering or semantic search.
 
 <!--- Describe your model here -->
+This specific model is fine-tuned for Issue Report Classification in 4 classes: bug, documentation, feature, question
 
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
+## Usage
 
-Then you can use the model like this:
+You can use the model like this:
 
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
+from sentence_transformers.losses import CosineSimilarityLoss
+from setfit import SetFitModel
+from setfit import SetFitTrainer
+sentences = ["error in line 20", "add method list_features"]
+
+label_mapping = {
+  0 : "bug",
+  1 : "documentation",
+  2 : "feature",
+  3 : "question"
+}
 
-`torch.utils.data.dataloader.DataLoader` of length 460 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+model = SetFitModel.from_pretrained('PeppoCola/FewShotIssueClassifier-NLBSE23')
+predictions = model.predict(sentences)
+print([label_mapping[i] for i in predictions])
 ```
 
-**Loss**:
+## Dataset
+This model is trained on a subset of the [NLBSE23](https://nlbse2023.github.io/tools/) dataset. The sample was hand-labeled, and made available on [Zenodo](https://zenodo.org/record/7628150#.ZBnM3XbMJD8)
 
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+## Citing & Authors
 
-Parameters of the fit()-Method:
 ```
-{
-    "epochs": 1,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": 460,
-    "warmup_steps": 46,
-    "weight_decay": 0.01
+@software{Colavito_Few-Shot_Learning_for_2023,
+author = {Colavito, Giuseppe and Lanubile, Filippo and Novielli, Nicole},
+month = {2},
+title = {{Few-Shot Learning for Issue Report Classification}},
+url = {https://github.com/collab-uniba/Issue-Report-Classification-NLBSE2023},
+version = {1.0.0},
+year = {2023}
 }
 ```
 
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Normalize()
-)
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+@dataset{colavito_giuseppe_2023_7628150,
+  author       = {Colavito Giuseppe and
+                  Lanubile Filippo and
+                  Novielli Nicole},
+  title        = {Few-Shot Learning for Issue Report Classification},
+  month        = feb,
+  year         = 2023,
+  note         = {{To use this, merge the CSV with the original 
+                   dataset (after removing duplicates on the 'id'
+                   column)}},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.7628150},
+  url          = {https://doi.org/10.5281/zenodo.7628150}
+}
+```