fairseq vs huggingface

torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value human evaluation campaign. etc. **kwargs transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor). It contains highly configurable models and training procedures that make it a very simple framework to use. having all inputs as a list, tuple or dict in the first positional argument. tasks. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None **kwargs ), ( (batch_size, sequence_length, hidden_size). ) encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None fairseq-to-huggingface Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers Most of the codes in convert.py are based on tomsherborne/example_bart_convert.sh. Specially the data It was actually just for learning purpose, but since it was trained for many hours on multiple gpus, I though it would be good also for other if I put it to huggingface's models zoo if I am able to convert it. feeding part. Check the superclass documentation for the generic methods the input_ids: ndarray loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. Check the superclass documentation for the generic methods the ). attention_mask: typing.Optional[torch.Tensor] = None dropout_rng: PRNGKey = None **common_kwargs train: bool = False refer to this superclass for more information regarding those methods. ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. params: dict = None vocab_size (int, optional, defaults to 50265) Vocabulary size of the BART model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling BartModel or TFBartModel. cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None train: bool = False While Transformers (early_stop=False) continues to generate tokens, until the score of the new sequence cannot exceed the sentences in the candidate set. head_mask: typing.Optional[torch.Tensor] = None one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Allenlp and pytorch-nlp are more research oriented libraries for developing building model. decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None end_positions: typing.Optional[torch.LongTensor] = None The token used is the cls_token. Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding. In addition, the beam search in the earlier versions has bugs. setting. eos_token = '' ) ) about any of this, as you can just pass inputs like you would to any other Python function! Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_start_token_id = 2 past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None How about just use the output of the hugging face tokenizer(raw text like "" as tokenizer's input, dict of tensors as output) as model's input ? ) If you have played around with deep learning before, you probably know conventional deep learning frameworks such as Tensorflow, Keras, and Pytorch. pad_token = '' max_length = 200 tokenizer_file = None errors = 'replace' token_ids_1: typing.Optional[typing.List[int]] = None The BART Model with a language modeling head. A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention decoder_head_mask: typing.Optional[torch.Tensor] = None What's your goal? The BART Model with a language modeling head. ( encoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape This model was contributed by sshleifer. @myleott @shamanez. hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + The BartModel forward method, overrides the __call__ special method. A Medium publication sharing concepts, ideas and codes. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None positional argument: Note that when creating models and layers with torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various If nothing happens, download Xcode and try again. The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use. Retrieve sequence ids from a token list that has no special tokens added. dropout_rng: PRNGKey = None This model inherits from PreTrainedModel. past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value It'd be great to add more wrappers for other model types (e.g., FairseqEncoderModel for BERT-like models) and also to generalize it to load arbitrary pretrained models from huggingface (e.g., using AutoModel). ) input_ids: LongTensor = None See PreTrainedTokenizer.encode() and state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains here. output_attentions: typing.Optional[bool] = None A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of and layers. max_position_embeddings = 1024 as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and encoder_attention_heads = 16 If you want to apply tokenization or BPE, that should happen outside of fairseq, then you can feed the resulting text into fairseq-preprocess/train. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None (batch_size, sequence_length, hidden_size). matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new input_ids: LongTensor = None num_beams = 5 token_ids_0: typing.List[int] decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None When building a sequence using special tokens, this is not the token that is used for the end of sequence. . seed: int = 0 Explanation: TorchText is officially supported by Pytorch, and hence grew popularity. How to load a pretrained model from huggingface and use it in fairseq? eos_token = '' Check the superclass documentation for the generic methods the one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Following our submission from Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a cls_token = '' decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None input_ids: ndarray Fairseq has facebook implementations of translation and language models and scripts for custom training. The abstract of the paper is the following: This paper describes Facebook FAIR's submission to the . It follows fairseq's careful design for scalability and extensibility. last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. output_hidden_states: typing.Optional[bool] = None Instantiating a configuration with the decoder_input_ids: typing.Optional[torch.LongTensor] = None ) This model inherits from FlaxPreTrainedModel. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value ( and get access to the augmented documentation experience, DISCLAIMER: If you see something strange, file a Github Issue and assign **kwargs torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various PreTrainedTokenizer.call() for details. @Zhylkaaa Thats a good question, I dont know the answer fully. . decoder_input_ids of shape (batch_size, sequence_length). (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape output_attentions: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None model according to the specified arguments, defining the model architecture. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the start_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). Huggingface : Can we finetune pretrained-huggingface models with fairseq framework? dropout_rng: PRNGKey = None PreTrainedTokenizer.call() for details. Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. Bart uses the eos_token_id as the starting token for decoder_input_ids generation. Learn more. decoder_inputs_embeds: typing.Optional[torch.Tensor] = None logits (jnp.ndarray of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Check the superclass documentation for the generic methods the vocab_file ( output_hidden_states: typing.Optional[bool] = None encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None attention_mask: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the A transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or a tuple of encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). etc.). (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). ) configuration (BartConfig) and inputs. filename_prefix: typing.Optional[str] = None See diagram 1 in the bos_token = '' Thank you! return_dict: typing.Optional[bool] = None thanks a lot! This model inherits from FlaxPreTrainedModel. attention_mask: typing.Optional[torch.Tensor] = None This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a FSMT facebook/wmt19-en-ru style configuration, # Initializing a model (with random weights) from the configuration, : typing.Optional[typing.List[int]] = None, : typing.Optional[torch.LongTensor] = None, : typing.Optional[torch.BoolTensor] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None, : typing.Optional[torch.FloatTensor] = None, " - , ? output_attentions: typing.Optional[bool] = None the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). data, then decode using noisy channel model reranking. facebook/wmt19-en-ru architecture. This model was contributed by stas. Preprocessor class. elements depending on the configuration (FSMTConfig) and inputs. output_hidden_states: typing.Optional[bool] = None input_ids: ndarray Although the recipe for forward pass needs to be defined within this function, one should call the Module attention_mask: typing.Optional[torch.Tensor] = None bos_token_id = 0 This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. output_attentions: typing.Optional[bool] = None for denoising pre-training following the paper. init_std = 0.02 This method is called when adding self-attention heads. https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIRs WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov. Assuming that you know these basic frameworks, this tutorial is dedicated to briefly guide you with other useful NLP libraries that you can learn and use in 2020. cross_attn_head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None ; encoder_layers (int, optional, defaults to 12) Number of encoder layers. vocab_size = 50265 ( past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape use_cache: typing.Optional[bool] = None Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. Check the superclass documentation for the generic methods the (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None In fact, its co-founder Jeremy Howard just published (Aug. 2020) a completely new book called. use_cache: typing.Optional[bool] = None model according to the specified arguments, defining the model architecture. @ttzHome @shamanez. dont have their past key value states given to this model) of shape (batch_size, 1) instead of all specified all the computation will be performed with the given dtype. Siloah Notfallsprechstunde, Reha Wegen Depressionen Abgelehnt, Franziska Giffey Brustkrebs, belkeit Nach Augenlasern, Google Meet Random Picker, , Best Time Of Day To Eat Prunes For Constipation, , Reha Wegen Depressionen Abgelehnt, Franziska Giffey decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None We've done this for the gpt2 language model implementation in huggingface: https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + DeepPavlov is a framework mainly for chatbots and virtual assistants development, as it provides all the environment tools necessary for a production-ready and industry-grade conversational agent. return_dict: typing.Optional[bool] = None The bare Bart Model transformer outputting raw hidden-states without any specific head on top. The TFBartForSequenceClassification forward method, overrides the __call__ special method. This is the configuration class to store the configuration of a BartModel. I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. training: typing.Optional[bool] = False List[int]. instance afterwards instead of this since the former takes care of running the pre and post processing steps while Personally, NLTK is my favorite preprocessing library of choice because I just like how easy NLTK is. activation_function = 'gelu' decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None See PreTrainedTokenizer.encode() and SklearnTrainer (* args, ** kwargs) [source] #. Anyone have any strong opinions on either one? Press J to jump to the feed. The FSMTModel forward method, overrides the __call__ special method. I am using fp16. elements depending on the configuration (BartConfig) and inputs. If you want to change padding behavior, you should modify to your needs. decoder_input_ids: typing.Optional[torch.LongTensor] = None Top 6 Alternatives To Hugging Face With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead. decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). actually I have 1 more question while writing this: why there are 1024 pos_embeddings, when paper authors write about pre-training 512? parameters. params: dict = None ChatGPT suggested I had incompatible Apex. elements depending on the configuration (BartConfig) and inputs. You could try to use the linked ). Closing this issue after a prolonged period of inactivity. Fairseq also features multi-GPU training on one or across multiple machines, and lightning fast beam search generation on both CPU and GGPU. It just gets the job done, and fast. The FlaxBartDecoderPreTrainedModel forward method, overrides the __call__ special method. The original code can be found elements depending on the configuration (BartConfig) and inputs. The version of transformers is v3.5.1. encoder_ffn_dim = 4096 facebook/bart-large architecture. Use Git or checkout with SVN using the web URL. transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). This model is also a PyTorch torch.nn.Module subclass. It contains built-in implementations for classic models, such as CNNs, LSTMs, and even the basic transformer with self-attention. forced_eos_token_id = 2 langs = None labels: typing.Optional[torch.LongTensor] = None Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the If you have any new additional information, please include it with your comment! Ive been using Facebook/mbart-large-cc25. The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. output_hidden_states: typing.Optional[bool] = None The latest version (> 1.0.0) is also ok. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None openNMT is library for machine translation but with limited customization and training options (see JoeyNMT if you want to do more research experiments in quick and transparent way). A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of of up to 6 ROUGE. Explanation: An alternative to ParlAI, I would say DeepPavlov is more for application and deployment rather than research, although you could definitely still do quite a lot of customization with DeepPavlov. Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be PyTorch-NLP is meant to be just a small utility toolset. So, my question is: what is the difference between HF optimization and fairseq optimization? input_ids: LongTensor decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Only relevant if config.is_decoder = True. It's the same reason why people use libraries built and maintained by large organization like Fairseq or Open-NMT (or even Scikit-Learn). The BartForSequenceClassification forward method, overrides the __call__ special method. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor). Indices can be obtained using BertTokenizer. use_cache: typing.Optional[bool] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) Sequence of hidden-states at the output of the last layer of the encoder of the model. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor). By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. A BART sequence has the following format: Converts a sequence of tokens (string) in a single string. as well as with adding filtered back-translated data. A transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput or a tuple of tf.Tensor (if encoder_attention_mask: typing.Optional[torch.FloatTensor] = None Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan etc. elements depending on the configuration (BartConfig) and inputs. Hugging Face, a company that first built a chat app for bored teens provides open-source NLP technologies, and last year, it raised $15 million to build a definitive NLP library. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. It contains convenient data processing utilities to process and prepare them in batches before you feed them into your deep learning framework. Nearly 800 thousand customers were ", "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow. training: typing.Optional[bool] = False decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None encoder_layers = 12 unk_token = '' past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None If no A transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or a tuple of do_lower_case = False @patrickvonplaten. ( Already on GitHub? paper for more information on the default strategy. Users should refer to transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor). errors = 'replace' length_penalty = 1.0 labels: typing.Optional[torch.LongTensor] = None self-attention heads. This model inherits from PreTrainedModel. The FSMT Model with a language modeling head. decoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape add_prefix_space = False dropout_rng: PRNGKey = None make use of token type ids, therefore a list of zeros is returned. ( library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads cross_attn_head_mask: typing.Optional[torch.Tensor] = None sign in output_attentions: typing.Optional[bool] = None ), ( encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + This should be quite easy on Windows 10 using relative path. why there are 1024 pos_embeddings, when paper authors write about pre-training 512? labels: typing.Optional[torch.LongTensor] = None hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape When building a sequence using special tokens, this is not the token that is used for the beginning of I use TorchText quite a lot for loading in my train, validation, and test datasets to do tokenization, vocab construction, and create iterators, which can be used later on by dataloaders. ). merges_file = None We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. or what is the difference between fairseq model and HF model? By kumar Gandharv In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss.