dictabert morph EU AI Act Compliance Profile

DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew

State-of-the-art language model for Hebrew, released here.

This is the fine-tuned model for the morphological tagging task.

For the bert-base models for other tasks, see here.

Sample usage:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-morph')
model = AutoModel.from_pretrained('dicta-il/dictabert-morph', trust_remote_code=True)

model.eval()

sentence = 'בשנת 1948 השלים אפרים קישון את לימודיו בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים'
print(model.predict([sentence], tokenizer))

Output:

[{
	"text": "בשנת 1948 השלים אפרים קישון את לימודיו בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים",
	"tokens": [{
		"token": "בשנת",
		"pos": "NOUN",
		"feats": {
			"Gender": "Fem",
			"Number": "Sing"
		},
		"prefixes": ["ADP"],
		"suffix": false
	}, {
		"token": "1948",
		"pos": "NUM",
		"feats": {},
		"prefixes": [],
		"suffix": false
	}, {
		"token": "השלים",
		"pos": "VERB",
		"feats": {
			"Gender": "Masc",
			"Number": "Sing",
			"Person": "3",
			"Tense": "Past"
		},
		"prefixes": [],
		"suffix": false
	}, {
		"token": "אפרים",
		"pos": "PROPN",
		"feats": {},
		"prefixes": [],
		"suffix": false
	}, {
		"token": "קישון",
		"pos": "PROPN",
		"feats": {},
		"prefixes": [],
		"suffix": false
	}, {
		"token": "את",
		"pos": "ADP",
		"feats": {},
		"prefixes": [],
		"suffix": false
	}, {
		"token": "לימודיו",
		"pos": "NOUN",
		"feats": {
			"Gender": "Masc",
			"Number": "Plur"
		},
		"prefixes": [],
		"suffix": "PRON",
		"suffix_feats": {
			"Gender": "Masc",
			"Number": "Sing",
			"Person": "3"
		}
	}, {
		"token": "בפיסול",
		"pos": "NOUN",
		"feats": {
			"Gender": "Masc",
			"Number": "Sing"
		},
		"prefixes": ["ADP"],
		"suffix": false
	}, {
		"token": "מתכת",
		"pos": "NOU

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