weigths*weights

docs/source/migration.md

@@ -27,7 +27,7 @@ loss = outputs[0]
 # In transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
-# And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
+# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
 model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
 outputs = model(input_ids, labels=labels)
 loss, logits, attentions = outputs

src/transformers/modeling_transfo_xl.py

@@ -136,7 +136,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
         if "kernel" in name or "proj" in name:
             array = np.transpose(array)
         if ("r_r_bias" in name or "r_w_bias" in name) and len(pointer) > 1:
-            # Here we will split the TF weigths
+            # Here we will split the TF weights
             assert len(pointer) == array.shape[0]
             for i, p_i in enumerate(pointer):
                 arr_i = array[i, ...]

src/transformers/modeling_xlnet.py

@@ -156,7 +156,7 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
             logger.info("Transposing")
             array = np.transpose(array)
         if isinstance(pointer, list):
-            # Here we will split the TF weigths
+            # Here we will split the TF weights
             assert len(pointer) == array.shape[0]
             for i, p_i in enumerate(pointer):
                 arr_i = array[i, ...]

templates/adding_a_new_model/README.md

@@ -59,4 +59,4 @@ You can then finish the addition step by adding imports for your classes in the
 - [ ] add a link to your conversion script in the main conversion utility (in `commands/convert.py`)
 - [ ] edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py` file
 - [ ] add a mention of your model in the doc: `README.md` and the documentation itself at `docs/source/pretrained_models.rst`.
-- [ ] upload the pretrained weigths, configurations and vocabulary files.
+- [ ] upload the pretrained weights, configurations and vocabulary files.