Documentation fixes (#7585)
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@ -114,8 +114,8 @@ class PretrainedConfig(object):
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model pretrained weights.
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- **finetuning_task** (:obj:`str`, `optional`) -- Name of the task used to fine-tune the model. This can be
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used when converting from an original (TensorFlow or PyTorch) checkpoint.
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- **id2label** (:obj:`List[str]`, `optional`) -- A map from index (for instance prediction index, or target
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index) to label.
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- **id2label** (:obj:`Dict[int, str]`, `optional`) -- A map from index (for instance prediction index, or
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target index) to label.
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- **label2id** (:obj:`Dict[str, int]`, `optional`) -- A map from label to index for the model.
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- **num_labels** (:obj:`int`, `optional`) -- Number of labels to use in the last layer added to the model,
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typically for a classification task.
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@ -455,11 +455,12 @@ PT_MASKED_LM_SAMPLE = r"""
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
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>>> input_ids = tokenizer("Hello, my dog is cute", return_tensors="pt")["input_ids"]
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>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
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>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
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>>> outputs = model(input_ids, labels=input_ids)
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>>> outputs = model(**inputs, labels=labels)
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>>> loss = outputs.loss
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>>> prediction_logits = outputs.logits
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>>> logits = outputs.logits
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"""
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PT_BASE_MODEL_SAMPLE = r"""
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@ -521,14 +522,15 @@ TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
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>>> input_ids = inputs["input_ids"]
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>>> inputs["labels"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1
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>>> outputs = model(inputs)
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>>> loss, scores = outputs[:2]
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>>> loss = outputs.loss
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>>> logits = outputs.logits
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"""
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TF_QUESTION_ANSWERING_SAMPLE = r"""
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@ -538,14 +540,16 @@ TF_QUESTION_ANSWERING_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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>>> input_dict = tokenizer(question, text, return_tensors='tf')
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>>> start_scores, end_scores = model(input_dict)
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>>> outputs = model(input_dict)
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>>> start_logits = outputs.start_logits
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>>> end_logits = outputs.end_logits
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>>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
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>>> answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
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>>> answer = ' '.join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0]+1])
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"""
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TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
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@ -555,13 +559,14 @@ TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
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>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
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>>> outputs = model(inputs)
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>>> loss, logits = outputs[:2]
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>>> loss = outputs.loss
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>>> logits = outputs.logits
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"""
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TF_MASKED_LM_SAMPLE = r"""
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@ -571,12 +576,14 @@ TF_MASKED_LM_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
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>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
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>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
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>>> outputs = model(input_ids)
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>>> prediction_scores = outputs[0]
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>>> outputs = model(inputs)
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>>> loss = outputs.loss
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>>> logits = outputs.logits
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"""
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TF_BASE_MODEL_SAMPLE = r"""
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@ -586,12 +593,12 @@ TF_BASE_MODEL_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
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>>> outputs = model(inputs)
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>>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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>>> last_hidden_states = outputs.last_hidden_states
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"""
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TF_MULTIPLE_CHOICE_SAMPLE = r"""
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@ -601,7 +608,7 @@ TF_MULTIPLE_CHOICE_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
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>>> choice0 = "It is eaten with a fork and a knife."
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@ -612,7 +619,7 @@ TF_MULTIPLE_CHOICE_SAMPLE = r"""
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>>> outputs = model(inputs) # batch size is 1
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>>> # the linear classifier still needs to be trained
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>>> logits = outputs[0]
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>>> logits = outputs.logits
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"""
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TF_CAUSAL_LM_SAMPLE = r"""
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@ -622,18 +629,21 @@ TF_CAUSAL_LM_SAMPLE = r"""
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>>> import tensorflow as tf
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>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}')
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>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True))
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
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>>> outputs = model(inputs)
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>>> logits = outputs[0]
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>>> logits = outputs.logits
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"""
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def add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None, output_type=None, config_class=None):
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def add_code_sample_docstrings(
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*docstr, tokenizer_class=None, checkpoint=None, output_type=None, config_class=None, mask=None
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):
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def docstring_decorator(fn):
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model_class = fn.__qualname__.split(".")[0]
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is_tf_class = model_class[:2] == "TF"
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doc_kwargs = dict(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)
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if "SequenceClassification" in model_class:
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code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE
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@ -643,7 +653,8 @@ def add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None, o
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code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE
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elif "MultipleChoice" in model_class:
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code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE
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elif "MaskedLM" in model_class:
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elif "MaskedLM" in model_class or model_class in ["FlaubertWithLMHeadModel", "XLMWithLMHeadModel"]:
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doc_kwargs["mask"] = "[MASK]" if mask is None else mask
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code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE
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elif "LMHead" in model_class:
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code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE
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@ -653,7 +664,7 @@ def add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None, o
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raise ValueError(f"Docstring can't be built for model {model_class}")
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output_doc = _prepare_output_docstrings(output_type, config_class) if output_type is not None else ""
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built_doc = code_sample.format(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)
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built_doc = code_sample.format(**doc_kwargs)
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fn.__doc__ = (fn.__doc__ or "") + "".join(docstr) + output_doc + built_doc
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return fn
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@ -1169,6 +1169,7 @@ class FunnelForMaskedLM(FunnelPreTrainedModel):
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checkpoint="funnel-transformer/small",
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output_type=MaskedLMOutput,
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config_class=_CONFIG_FOR_DOC,
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mask="<mask>",
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)
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def forward(
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self,
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@ -847,6 +847,7 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
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checkpoint="roberta-base",
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output_type=MaskedLMOutput,
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config_class=_CONFIG_FOR_DOC,
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mask="<mask>",
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)
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def forward(
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self,
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@ -706,6 +706,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
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checkpoint="xlm-mlm-en-2048",
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output_type=MaskedLMOutput,
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config_class=_CONFIG_FOR_DOC,
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mask="<special1>",
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)
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def forward(
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self,
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