Version 0.3.4

• support multiple GPU training/inference (--multi-gpu parameter)
• support safetensors model weights format
• support private HuggingFace models
• in application scripts: add max-epoch parameter, learning rate parameter
• add grobid model for funding and acknowledgement information
• more parameter information printed when training
• some dependency updates

Version 0.3.3

with PyPI:

pip install delft==0.3.3

• support for incremental training
• fix SciBERT tokenier initialization from HuggingFace model
• updated HuggingFace transformers library to 4.25.1 and tensorflow to 2.9.3
• review the support of BPE tokenizers in the case of pre-tokenized input with the updated transformers library for most transformer models using it (tested with Roberta/GPT2, CamemBERT, bart-base, albert-base-v2, and XLM)
• addition of some model variants for sequence labeling (BERT_FEATURES, BERT_ChainCRF_FEATURES)

Version 0.3.2

with PyPI:

pip install delft==0.3.2

• Print model parameters at creation and load time
• Dataset recognition
• Model updates
• Set feature channel embeddings trainable

Full Changelog: v0.3.1...v0.3.2

Version 0.3.1

• fix a problem with CRF tensorflow-addons when batch size is 1

Version 0.3.0

• Migration of DeLFT to TensorFlow 2.7
• Support of HuggingFace transformer library (auto* library)
• New architectures and updated models
• General usage of optimizer with learning rate decay
• Updated docs now via readthedoc
• Improved ELMo embeddings
• Transformers wrapper to limit usage of Hugging Face hub only necessary, model with transformer layer fully portable without hub access

Version 0.2.6

• add automatic download of embeddings if not locally available
• enable embedding preload script for docker image

Version 0.2.5

• fix serialization of models with feature preprocessor (PR #110)
• update grobid models with features
• some other models and score updates
• add "software was used" classification model for software citations
• update tensorflow dependency

Version 0.2.4

• generic support for feature channel in sequence labeling, test with Grobid training data
• fix issues #40 #44 #48 #50 #52 #54 #56 #66 #69 #71 #94 #100 #103
• update eval (average field level n-fold cross-validation)
• dataseer and software use classification models
• review and improvement for BERT sequence labeling and classification (unicode, binary/multi-label, test SciBERT, bioBERT, ...)
• force split lemonde corpus evaluation (to be compared with some publication results using this)
• fixing truncation in sequence labeling
• more documentation
• various bug fixing