uv handles dependencies

.github/workflows/tests.yml

@@ -55,8 +55,6 @@ jobs:
           python -m pip install --no-cache-dir --upgrade pip
           python -m pip install --no-cache-dir ${{ matrix.requirements }}
           python -m pip install '.[codecarbon]'
-          python -m spacy download en_core_web_lg
-          python -m spacy download en_core_web_sm
         if: steps.restore-cache.outputs.cache-hit != 'true'
 
       - name: Install the checked-out setfit

README.md

@@ -149,7 +149,6 @@ make style && make quality
 
 * [https://github.com/pmbaumgartner/setfit](https://github.com/pmbaumgartner/setfit) - A scikit-learn API version of SetFit.
 * [jxpress/setfit-pytorch-lightning](https://github.com/jxpress/setfit-pytorch-lightning) - A PyTorch Lightning implementation of SetFit.
-* [davidberenstein1957/spacy-setfit](https://github.com/davidberenstein1957/spacy-setfit) - An easy and intuitive approach to use SetFit in combination with spaCy. 
 
 ## Citation
 

docs/source/en/_toctree.yml

@@ -35,8 +35,6 @@
     title: Knowledge Distillation
   - local: how_to/batch_sizes
     title: Batch Sizes for Inference
-  - local: how_to/absa
-    title: Aspect Based Sentiment Analysis
   - local: how_to/v1.0.0_migration_guide
     title: v1.0.0 Migration Guide
   title: How-to Guides

docs/source/en/how_to/v1.0.0_migration_guide.mdx

@@ -39,7 +39,6 @@ This list contains new functionality that can be used starting from v1.0.0.
     * `use_labels` (defaults to `True`): Whether to use the `SetFitModel.labels` to convert integer labels to string labels. Not used if the training labels are already strings.
 * [`SetFitModel.encode`] has been introduce to convert input sentences to embeddings using the `SentenceTransformer` body.
 * [`SetFitModel.device`] has been introduced to determine the device of the model.
-* [`AbsaTrainer`] and [`AbsaModel`] have been introduced for applying [SetFit for Aspect Based Sentiment Analysis](absa).
 * [`Trainer`] now supports a `callbacks` argument for a list of [`transformers` `TrainerCallback` instances](https://huggingface.co/docs/transformers/main/en/main_classes/callback).
     * By default, all installed callbacks integrated with `transformers` are supported, including [`TensorBoardCallback`](https://huggingface.co/docs/transformers/main/en/main_classes/callback#transformers.integrations.TensorBoardCallback), [`WandbCallback`](https://huggingface.co/docs/transformers/main/en/main_classes/callback#transformers.integrations.WandbCallback) to log training logs to [TensorBoard](https://www.tensorflow.org/tensorboard) and [W&B](https://wandb.ai), respectively.
     * The [`Trainer`] will now print `embedding_loss` in the terminal, as well as `eval_embedding_loss` if `eval_strategy` is set to `"epoch"` or `"steps"` in [`TrainingArguments`].
@@ -90,4 +89,3 @@ This list contains new functionality that can be used starting from v1.0.0.
     the case it is "steps", `save_steps` must be a round multiple of `eval_steps`.
 
     </Tip>
-* Pushing SetFit or SetFitABSA models to the Hub with [`SetFitModel.push_to_hub`] or [`AbsaModel.push_to_hub`] now results in a detailed model card. As an example, see [this SetFitModel](https://huggingface.co/tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot) or [this SetFitABSA polarity model](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity).

docs/source/en/reference/main.mdx

@@ -21,36 +21,3 @@
 [[autodoc]] SetFitModelCardData
     - to_dict
     - to_yaml
-
-## AbsaModel
-
-[[autodoc]] AbsaModel
-    - __call__
-    - device
-    - from_pretrained
-    - predict
-    - push_to_hub
-    - to
-    - save_pretrained
-
-### AspectModel
-
-[[autodoc]] AspectModel
-    - __call__
-    - device
-    - from_pretrained
-    - predict
-    - push_to_hub
-    - save_pretrained
-    - to
-
-### PolarityModel
-
-[[autodoc]] PolarityModel
-    - __call__
-    - device
-    - from_pretrained
-    - predict
-    - push_to_hub
-    - save_pretrained
-    - to

docs/source/en/reference/trainer.mdx

@@ -36,15 +36,3 @@
     - train
     - train_classifier
     - train_embeddings
-
-## AbsaTrainer
-
-[[autodoc]] AbsaTrainer
-    - add_callback
-    - evaluate
-    - pop_callback
-    - push_to_hub
-    - remove_callback
-    - train
-    - train_aspect
-    - train_polarity