Updates for transformers v4.31.0 compatibility

CHANGELOG.md

@@ -6,6 +6,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 
+## [0.64.0] - 2023-04-24
+
+### Changed
+
+- Updates for compatibility with transformers v4.31.0
+
+### Fixed
+
+- Fixed bug in `ClassificationModel` when using `sliding_window`
 
 ## [0.63.11] - 2023-04-24
 

docs/_docs/01-installation.md

@@ -22,13 +22,16 @@ Follow the instructions given below to install Simple Transformers using with An
 3. Using a CUDA capable GPU is recommended.
    To install Pytorch with CUDA support:
    ```shell
-   conda install pytorch>=1.6 cudatoolkit=11.0 -c pytorch
+   conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
    ```
    CPU only:
    ```shell
    conda install pytorch cpuonly -c pytorch
    ```
 
+   **Note:** Check the [Pytorch website](https://pytorch.org/get-started/locally/) for the latest instructions.
+    {: .notice--info}
+
 4. Install simpletransformers.
 `pip install simpletransformers`
 

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="simpletransformers",
-    version="0.63.11",
+    version="0.64.0",
     author="Thilina Rajapakse",
     author_email="chaturangarajapakshe@gmail.com",
     description="An easy-to-use wrapper library for the Transformers library.",
@@ -26,7 +26,7 @@
         "requests",
         "tqdm>=4.47.0",
         "regex",
-        "transformers>=4.6.0",
+        "transformers>=4.31.0",
         "datasets",
         "scipy",
         "scikit-learn",

simpletransformers/classification/classification_model.py

@@ -2237,19 +2237,26 @@ def predict(self, to_predict, multi_label=False):
                     final_preds = []
                     for pred_row in preds:
                         mean_pred = np.mean(pred_row)
-                        print(mean_pred)
+                        # print(mean_pred)
                         final_preds.append(mean_pred)
-                    preds = np.array(final_preds)
                 else:
                     preds = [np.argmax(pred, axis=1) for pred in preds]
                     final_preds = []
                     for pred_row in preds:
-                        mode_pred, counts = mode(pred_row)
-                        if len(counts) > 1 and counts[0] == counts[1]:
+                        val_freqs_desc = Counter(pred_row).most_common()
+                        if (
+                            len(val_freqs_desc) > 1
+                            and val_freqs_desc[0][1] == val_freqs_desc[1][1]
+                        ):
                             final_preds.append(args.tie_value)
                         else:
-                            final_preds.append(mode_pred[0])
-                    preds = np.array(final_preds)
+                            final_preds.append(val_freqs_desc[0][0])
+                        # mode_pred, counts = mode(pred_row)
+                        # if len(counts) > 1 and counts[0] == counts[1]:
+                        #     final_preds.append(args.tie_value)
+                        # else:
+                        #     final_preds.append(mode_pred[0])
+                preds = np.array(final_preds)
             elif not multi_label and args.regression is True:
                 preds = np.squeeze(preds)
                 model_outputs = preds

simpletransformers/classification/multi_modal_classification_model.py

@@ -44,7 +44,7 @@
     BertModel,
     BertTokenizer,
 )
-from transformers.models.mmbt.configuration_mmbt import MMBTConfig
+from transformers.models.deprecated.mmbt.configuration_mmbt import MMBTConfig
 
 from simpletransformers.classification.classification_utils import (
     ImageEncoder,

simpletransformers/classification/transformer_models/mmbt_model.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
-from transformers.models.mmbt.modeling_mmbt import MMBTModel
+from transformers.models.deprecated.mmbt.modeling_mmbt import MMBTModel
 
 
 class MMBTForClassification(nn.Module):

train.txt

@@ -0,0 +1,100 @@
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers! 
+Hello world with Simple Transformers!