Add hugs

docs/source/index.rst

@@ -42,7 +42,7 @@ The documentation is organized in five parts:
 
 - **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
   and a glossary.
-- **USING TRANSFORMERS** contains general tutorials on how to use the library.
+- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
 - **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
 - **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
   transformers model
@@ -135,7 +135,7 @@ conversion utilities for the following models:
 
 .. toctree::
     :maxdepth: 2
-    :caption: Using Transformers
+    :caption: Using 🤗 Transformers
 
     task_summary
     model_summary

docs/source/migration.md

@@ -1,8 +1,8 @@
 # Migrating from previous packages
 
-## Migrating from pytorch-transformers to transformers
+## Migrating from pytorch-transformers to 🤗 Transformers
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
+Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
 
 ### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
 
@@ -14,17 +14,17 @@ If you used to call the models with positional inputs for keyword arguments, e.g
 
 ## Migrating from pytorch-pretrained-bert
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
+Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers
 
 ### Models always output `tuples`
 
-The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
+The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
 
 The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
 
 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
 
-Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
+Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model:
 
 ```python
 # Let's load our model
@@ -33,11 +33,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 # If you used to have this line in pytorch-pretrained-bert:
 loss = model(input_ids, labels=labels)
 
-# Now just use this line in transformers to extract the loss from the output tuple:
+# Now just use this line in 🤗 Transformers to extract the loss from the output tuple:
 outputs = model(input_ids, labels=labels)
 loss = outputs[0]
 
-# In transformers you can also have access to the logits:
+# In 🤗 Transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
 # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
@@ -109,7 +109,7 @@ for batch in train_data:
     loss.backward()
     optimizer.step()
 
-### In Transformers, optimizer and schedules are splitted and instantiated like this:
+### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
 ### and used like this:

docs/source/model_summary.rst

@@ -1,7 +1,7 @@
 Summary of the models
 ================================================
 
-This is a summary of the models available in the transformers library. It assumes you’re familiar with the original 
+This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original 
 `transformer model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer 
 <http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
 models. You can check them more in detail in their respective documentation. Also checkout the 

docs/source/philosophy.rst

@@ -1,7 +1,7 @@
 Philosophy
 ==========
 
-Transformers is an opinionated library built for:
+🤗 Transformers is an opinionated library built for:
 
 - NLP researchers and educators seeking to use/study/extend large-scale transformers models
 - hands-on practitioners who want to fine-tune those models and/or serve them in production

docs/source/quicktour.rst

@@ -44,27 +44,27 @@ make them readable. For instance
 
 ::
 
-    classifier('We are very happy to show you the Transformers library.')
+    classifier('We are very happy to show you the 🤗 Transformers library.')
 
 will return something like this:
 
 ::
 
-    [{'label': 'POSITIVE', 'score': 0.999799370765686}]
+    [{'label': 'POSITIVE', 'score': 0.9997795224189758}]
 
 That's encouraging! You can use it on a list of sentences, which will be preprocessed then fed to the model as a
 `batch`:
 
 ::
 
-    classifier(["We are very happy to show you the Transformers library.",
+    classifier(["We are very happy to show you the 🤗 Transformers library.",
                 "We hope you don't hate it."])
 
 returning a list of dictionaries like this one:
 
 ::
 
-    [{'label': 'POSITIVE', 'score': 0.999799370765686},
+    [{'label': 'POSITIVE', 'score': 0.9997795224189758},
      {'label': 'NEGATIVE', 'score': 0.5308589935302734}]
 
 You can see the second sentence has been classified as negative (it needs to be positive or negative) but its score is
@@ -163,7 +163,7 @@ To apply these steps on a given text, we can just feed it to our tokenizer:
 
 ::
 
-    input = tokenizer("We are very happy to show you the Transformers library.")
+    input = tokenizer("We are very happy to show you the 🤗 Transformers library.")
     print(input)
 
 This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__,
@@ -172,9 +172,8 @@ as mentioned before, but also additional arguments that will be useful to the mo
 
 
 ::
-
-    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 19081, 3075, 1012, 102],
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
 
 You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
 batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
@@ -184,13 +183,13 @@ and get tensors back. You can specify all of that to the tokenizer:
 
     ## PYTORCH CODE
     batch = tokenizer(
-        ["We are very happy to show you the Transformers library.",
+        ["We are very happy to show you the 🤗 Transformers library.",
          "We hope you don't hate it."],
         padding=True, truncation=True, return_tensors="pt")
     print(batch)
     ## TENSORFLOW CODE
     batch = tokenizer(
-        ["We are very happy to show you the Transformers library.",
+        ["We are very happy to show you the 🤗 Transformers library.",
          "We hope you don't hate it."],
         padding=True, truncation=True, return_tensors="tf")
     print(batch)
@@ -200,10 +199,10 @@ padding token the model was pretrained with. The attention mask is also adapted
 
 ::
 
-    {'input_ids': tensor([[  101,  2057,  2024,  2200,  3407,  2000,  2265,  2017,  1996, 19081, 3075,  1012,   102],
-                          [  101,  2057,  3246,  2017,  2123,  1005,  1056,  5223,  2009,  1012,  102,     0,     0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])}
+    {'input_ids': tensor([[  101,  2057,  2024,  2200,  3407,  2000,  2265,  2017,  1996,   100, 19081,  3075,  1012,   102],
+                          [  101,  2057,  3246,  2017,  2123,  1005,  1056,  5223,  2009,  1012,   102,     0,     0,     0]]), 
+     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])}
 
 You can learn more about tokenizers on their :doc:`doc page <main_classes/tokenizer>` (tutorial coming soon).
 

docs/source/task_summary.rst

@@ -217,9 +217,9 @@ Here is an example of question answering using a model and a tokenizer. The proc
     """
 
     questions = [
-        "How many pretrained models are available in Transformers?",
-        "What does Transformers provide?",
-        "Transformers provides interoperability between which frameworks?",
+        "How many pretrained models are available in 🤗 Transformers?",
+        "What does 🤗 Transformers provide?",
+        "🤗 Transformers provides interoperability between which frameworks?",
     ]
 
     for question in questions:
@@ -253,9 +253,9 @@ Here is an example of question answering using a model and a tokenizer. The proc
     """
 
     questions = [
-        "How many pretrained models are available in Transformers?",
-        "What does Transformers provide?",
-        "Transformers provides interoperability between which frameworks?",
+        "How many pretrained models are available in 🤗 Transformers?",
+        "What does 🤗 Transformers provide?",
+        "🤗 Transformers provides interoperability between which frameworks?",
     ]
 
     for question in questions:
@@ -280,13 +280,13 @@ This outputs the questions followed by the predicted answers:
 
 ::
 
-    Question: How many pretrained models are available in Transformers?
+    Question: How many pretrained models are available in 🤗 Transformers?
     Answer: over 32 +
 
-    Question: What does Transformers provide?
+    Question: What does 🤗 Transformers provide?
     Answer: general - purpose architectures
 
-    Question: Transformers provides interoperability between which frameworks?
+    Question: 🤗 Transformers provides interoperability between which frameworks?
     Answer: tensorflow 2 . 0 and pytorch
 
 

docs/source/torchscript.rst

@@ -12,7 +12,7 @@ According to Pytorch's documentation: "TorchScript is a way to create serializab
 Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
 their model to be re-used in other programs, such as efficiency-oriented C++ programs.
 
-We have provided an interface that allows the export of `transformers` models to TorchScript so that they can
+We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
 be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
 they can be exported, and what to be mindful of when using these models with TorchScript.
 

examples/README.md

@@ -1,6 +1,6 @@
 ## Examples
 
-Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
+Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
 Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.0+.
 
 Here is the list of all our examples:

notebooks/README.md

@@ -1,13 +1,13 @@
-# Transformers Notebooks
+# 🤗 Transformers Notebooks
 
 You can find here a list of the official notebooks provided by Hugging Face.
 
 Also, we would like to list here interesting content created by the community. 
-If you wrote some notebook(s) leveraging transformers and would like be listed here, please open a 
+If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a 
 Pull Request so it can be included under the Community notebooks. 
 
 
-## Hugging Face's notebooks :hugs:
+## Hugging Face's notebooks 🤗
 
 | Notebook     |      Description      |   |
 |:----------|:-------------|------:|