.. _information_retrieval: BERT Embedding Models ===================== Sentence-BERT (SBERT) is a modification of the BERT model that is specifically trained to generate semantically meaningful sentence embeddings. The model architecture and pre-training process are detailed in the `Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks `__ paper. Similar to BERT, Sentence-BERT utilizes a BERT-based architecture, but it is trained using a Siamese and triplet network structure to derive fixed-sized sentence embeddings that capture semantic information. Sentence-BERT is commonly used to generate high-quality sentence embeddings for various downstream natural language processing tasks, such as semantic textual similarity, clustering, and information retrieval Data Input for the Sentence-BERT model -------------------------------------- The fine-tuning data for the Sentence-BERT (SBERT) model should consist of data instances, each comprising a query, a positive document, and a list of negative documents. Negative mining is not supported in NeMo yet; therefore, data preprocessing should be performed offline before training. The dataset should be in JSON format. For instance, the dataset should have the following structure: .. code-block:: python [ { "query": "Query", "pos_doc": "Positive", "neg_doc": ["Negative_1", "Negative_2", ..., "Negative_n"] }, { // Next data instance }, ..., { // Subsequent data instance } ] This format ensures that the fine-tuning data is appropriately structured for training the Sentence-BERT model. Fine-tuning the Sentence-BERT model ----------------------------------- For fine-tuning Sentence-BERT model, you need to initialize the Sentence-BERT model with BERT model checkpoint. To do so, you should either have a ``.nemo`` checkpoint or need to convert a HuggingFace BERT checkpoint to NeMo (mcore) using the following: .. code-block:: python python NeMo/scripts/nlp_language_modeling/convert_bert_hf_to_nemo.py \ --input_name_or_path "intfloat/e5-large-unsupervised" \ --output_path /path/to/output/nemo/file.nemo \ --mcore True \ --precision 32 Then you can fine-tune the sentence-BERT model using the following script: .. code-block:: bash #!/bin/bash PROJECT= # wandb project name NAME= # wandb run name export WANDB_API_KEY= # your_wandb_key NUM_DEVICES=1 # number of gpus to train on CONFIG_PATH="/NeMo/examples/nlp/information_retrieval/conf/" CONFIG_NAME="megatron_bert_embedding_config" PATH_TO_NEMO_MODEL= # Path to conveted nemo model from hf TRAIN_DATASET_PATH= # Path to json dataset VALIDATION_DATASET_PATH= # Path to validation dataset SAVE_DIR= # where the checkpoint and logs are saved mkdir -p $SAVE_DIR export NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 python NeMo/examples/nlp/information_retrieval/megatron_bert_embedding_finetuning.py \ --config-path=${CONFIG_PATH} \ --config-name=${CONFIG_NAME} \ restore_from_path=${PATH_TO_NEMO_MODEL} \ trainer.devices=${NUM_DEVICES} \ trainer.max_steps=10000 \ trainer.val_check_interval=100 \ trainer.max_epochs=1 \ +trainer.num_sanity_val_steps=0 \ model.mcore_bert=True \ model.post_process=False \ model.global_batch_size=8 \ # should be NUM_DEVICES * model.micro_batch_size model.micro_batch_size=8 \ model.attention_backend="unfused" \ model.optim.lr=0.000005 \ model.optim.sched.min_lr=0.00000001 \ model.optim.sched.warmup_steps=100 \ model.encoder_seq_length=512 \ model.tokenizer.library="huggingface" \ model.tokenizer.type="intfloat/e5-large-unsupervised" \ model.data.data_train=${TRAIN_DATASET_PATH} \ model.data.data_validation=${VALIDATION_DATASET_PATH} \ model.data.hard_negatives_to_train=4 \ exp_manager.explicit_log_dir=${SAVE_DIR} \ exp_manager.create_wandb_logger=True \ exp_manager.resume_if_exists=True \ exp_manager.wandb_logger_kwargs.name=${NAME} \ exp_manager.wandb_logger_kwargs.project=${PROJECT} GPT Embedding Models ===================== Recent work has also shown that it is possible to use Decoder-Only (GPT Style) models to train embedding models. `Improving Text Embeddings with Large Language Models `__ is one such recent papers which served as inspiration to implement Decoder-only embedding training in Nemo. Training a GPT Embedding Model ------------------------------- To train GPT Embedding models we follow a format very similar to the SBERT Embedding training. However, there are a couple of differences. GPT Embedding model training expects a `jsonl` file in which each line is a json object. Here is a truncated example of data jsonl file:: {"query": "What did ... 1952-2002 period?", "pos_doc": "Morning (2008) ... has changed little.", "neg_doc": "Even though ... sapiens.", "query_id": "q103151", "doc_id": "d14755"} {"query": "What type of ... passions?", "pos_doc": "Burke was a leading ... upper classes.", "neg_doc": "Writing to a friend ... Government.", "query_id": "q77959", "doc_id": "d11263"} {"query": "Since 1999, ... progressed at?", "pos_doc": "Commercial solar water ... as of 2007.", "neg_doc": "The potential solar ... acquire.", "query_id": "q16545", "doc_id": "d1883"} As visible the json object should contain the following fields ``query``, ``pos_doc``, ``neg_doc``, ``query_id`` and ``doc_id``. The ``query_id`` and ``doc_id`` can be any alphanumeric string that uniquely maps to the ``query`` string and ``pos_doc`` string. During training, the GPT Embedding model employs LoRA (by default) to learn embeddings for the queries and documents, such that similarity of the ``query``-to-``pos_doc`` are maximized while simultaneously minimizing ``query``-to-``neg_doc`` similarity. LoRA allows us to fine-tune large LLMs such as Mistral 7B model with a relatively small number of training parameters. An example command to launch a training job is .. code-block:: console python3 /NeMo/examples/nlp/information_retrieval/megatron_gpt_embedding_finetuning.py \ exp_manager.exp_dir="PATH_TO_SAVE_LORA_WEIGHTS" \ model.global_batch_size=4 \ # exact choice for global batch size is data dependent typical values are in the range of 32 to 128. model.micro_batch_size=4 \ # exact choice for micro batch size is GPU memory dependent 2 to 8 are reasonable values. trainer.devices=1 \ # indicates how many GPUs to use during training per node. trainer.num_nodes=1 \ # indicates how many nodes to use if multi-node cluster is available trainer.max_steps=20 \ # how many training steps to run. model.restore_from_path="PATH_TO_BASE_NEMO_MODEL" \ model.peft.lora_tuning.adapter_dim=16 \ # the low-rank size for lora weights. model.data.train_ds.file_names=["train.jsonl"] The full list of possible run arguments is configurable in ``/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_tuning_config.yaml``. By default a trained model file should be generated in here ``PATH_TO_SAVE_LORA_WEIGHTS/megatron_gpt_peft_lora_tuning/checkpoints/`` typically with the extension ``.nemo``. Inference using a GPT Embedding Model ------------------------------------- Once trained, the GPT Embedding Model can be used to generate embeddings for queries and corpus documents. We can launch inference using the following command: .. code-block:: console python3 /NeMo/examples/nlp/information_retrieval/megatron_gpt_embedding_generate.py \ model.global_batch_size=4 \ model.micro_batch_size=4 \ trainer.devices=1 \ trainer.num_nodes=1 \ model.restore_from_path="PATH_TO_BASE_NEMO_MODEL" \ # Same base model used at training time. model.peft.restore_from_path="PATH_TO_SAVE_LORA_WEIGHTS/megatron_gpt_peft_lora_tuning/checkpoints//megatron_gpt_peft_lora_tuning.nemo" \ model.data.test_ds.query_file_names=["test_query.jsonl"] \ model.data.test_ds.doc_file_names=\["test_docs.jsonl"] \ model.data.test_ds.write_embeddings_to_file=True \ model.data.test_ds.output_file_path_prefix="PATH_TO_SAVE_EMEBDDINGS" The contents of ``test_queries.jsonl`` is expected to be in the following format:: {"query": "What do ... quantities?","query_id": "q11600", "doc_id": "d1172"} {"query": "What are ... subsectors?", "query_id": "q5831", "doc_id": "d577"} {"query": "Which article ... Government?", "query_id": "q3037", "doc_id": "d336"} Here, the ``doc_id`` field is expected to be the id of the document/passage which is the correct passage for the query. Note that since we are in inference mode, we don't require query-doc pairs. The contents of ``test_docs.jsonl`` is expected to be in the following format:: {"pos_doc": "Hormones ... vitamin D.", "doc_id": "d823"} {"pos_doc": "Historically, Victoria ... October 2016.", "doc_id": "d159"} {"pos_doc": "Exceptional examples ... Warsaw.", "doc_id": "d1084"} Once again, we show 3 examples form each file. Typically the ``test_docs.jsonl`` will contain more items than queries in the ``test_queries.jsonl``. The inference command will result in two folders * ``PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_queries`` * ``PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_docs`` The ``X`` in the folder ``consumed_samplesX`` is a number denoted number of batches consumed, this is not crucial at test time, but it is useful in training which we will see in the next section. First, let's take a look at the ``test_queries``. .. code-block:: console $> ls PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_queries query.ids query.npy $>head -n3 PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_queries/query.ids q11600 q5831 q3037 ``query.npy`` is a numpy pickled array containing rows of query embeddings and the ``query.ids`` text file list the id of each embedding in the same order. Similarly let's look into the ``test_docs`` folder .. code-block:: console $> ls PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_doc/ doc.ids doc.npy $> head -n3 PATH_TO_SAVE_EMBEDDINGS/consumed_samplesX/test_doc/doc.ids d823 d159 d1084 We can see that ``test_doc`` has a similar structure to ``test_queries`` but with ids and embeddings of the documents from the ``test_docs.josnl`` file. With this setup it is possible to evaluate the performance using metrics like MRR or NDCG.