Release new GliNER models

[ljvmiranda921]

Reference: #40

Release new GLiNER models

• https://github.com/urchade/GLiNER

The evaluation results for TLUnified-NER are shown in the table below (reported numbers are F1-scores):

	PER	ORG	LOC	Overall
tl_gliner_small	86.76	78.72	86.78	84.83
tl_gliner_medium	87.46	79.71	86.75	85.40
tl_gliner_large	86.75	80.20	86.76	85.72
tl_calamancy_trf	91.95	84.84	88.92	88.03
span-marker	92.57	82.04	90.56	89.62

In general, GliNER gets decent scores, but nothing beats regular finetuning on BERT-based models as seen in tl_calamancy_trf and span_marker. The performance on Universal NER is generally worse (the highest is around ~50%), compared to the reported results in the Universal NER paper (we finetuned on RoBERTa as well). One possible reason is that the annotation guidelines for TULunified-NER are more loose, because we consider some entities that Universal NER ignores. At the same time, the text distribution of the two datasets are widely different.

Nevertheless, I'm still releasing these GliNER models as they are very extensible to other entity types (and it's also nice to have a finetuned version of GliNER for Tagalog!). I haven't done any extensive hyperparameter tuning here so it might be nice if someone can contribute better config parameters to bump up these scores.