fit ( train_objectives: typing.Iterable], evaluator: typing.Optional = None, epochs: int = 1, steps_per_epoch=None, scheduler: str = 'WarmupLinear', warmup_steps: int = 10000, optimizer_class: typing.Type =, optimizer_params: typing.Dict =, weight_decay: float = 0.01, evaluation_steps: int = 0, output_path: typing.Optional = None, save_best_model: bool = True, max_grad_norm: float = 1, use_amp: bool = False, callback: typing.Optional, None]] = None, show_progress_bar: bool = True, checkpoint_path: typing.Optional = None, checkpoint_save_steps: int = 500, checkpoint_save_total_limit: int = 0 ) ¶ Initializes internal Module state, shared by both nn.Module and ScriptModule.
RATE IT IN A SENTENCE DOWNLOAD
Use_auth_token – HuggingFace authentication token to download private models. Modules – This parameter can be used to create custom SentenceTransformer models from scratch.ĭevice – Device (like ‘cuda’ / ‘cpu’) that should be used for computation. If that fails, tries to construct a model from Huggingface models repository with that name. If it is not a path, it first tries to download a pre-trained SentenceTransformer model. Model_name_or_path – If it is a filepath on disc, it loads the model from that path. Loads or create a SentenceTransformer model, that can be used to map sentences / text to embeddings. SentenceTransformer ( model_name_or_path : Optional = None, modules : Optional ] = None, device : Optional = None, cache_folder : Optional = None, use_auth_token : Optional ] = None ) ¶ The fit method accepts the following parameter: class sentence_transformers. We can pass more than one tuple in order to perform multi-task learning on several datasets with different loss functions. We pass a list of train_objectives, which consist of tuples (dataloader, loss_function). We tune the model by calling model.fit(). fit ( train_objectives =, epochs = 1, warmup_steps = 100 ) CosineSimilarityLoss ( model ) #Tune the model model.
train_examples =, label = 0.8 ), InputExample ( texts =, label = 0.3 )] #Define your train dataset, the dataloader and the train loss train_dataloader = DataLoader ( train_examples, shuffle = True, batch_size = 16 ) train_loss = losses.
Either from scratch of by loading a pre-trained model model = SentenceTransformer ( 'distilbert-base-nli-mean-tokens' ) #Define your train examples. This allows our network to be fine-tuned and to recognize the similarity of sentences.Ī minimal example with CosineSimilarityLoss is the following:įrom sentence_transformers import SentenceTransformer, InputExample, losses from import DataLoader #Define the model. The similarity of these embeddings is computed using cosine similarity and the result is compared to the gold similarity score. We can then train the network with a Siamese Network Architecture (for details see: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks)įor each sentence pair, we pass sentence A and sentence B through our network which yields the embeddings u und v. The most simple way is to have sentence pairs annotated with a score indicating their similarity, e.g. To fine-tune our network, we need somehow to tell our network which sentence pairs are similar, and should be close in vector space, and which pairs are dissimilar, and should be far away in vector space. Which loss function is suitable depends on the available training data and on the target task. Sadly there is no “one size fits all” loss function. It determines how well our embedding model will work for the specific downstream task. The loss function plays a critical role when fine-tuning the model. We can also construct more complex models: The output is then passed to the second entry ( pooling_model), which then returns our sentence embedding. Input text are first passed to the first entry ( word_embedding_model). For the modules parameter, we pass a list of layers which are executed consecutively. We create a new SentenceTransformer model by calling SentenceTransformer(modules=). Further, we create a (mean) pooling layer. We limit that layer to a maximal sequence length of 256, texts longer than that will be truncated. get_word_embedding_dimension ()) model = SentenceTransformer ( modules = )įirst we define our individual layers, in this case, we define ‘bert-base-uncased’ as the word_embedding_model. Transformer ( 'bert-base-uncased', max_seq_length = 256 ) pooling_model = models. Loading Custom SentenceTransformer Modelsįrom sentence_transformers import SentenceTransformer, models word_embedding_model = models.