# alephbertgimmel
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.

NOTE: This model was only trained with sequences of up to 128 tokens.

When using AlephBertGimmel, please reference:

Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 [http://arxiv.org/abs/2211.15199]



{}
transformers pytorch safetensors bert fill-mask arxiv:2211.15199 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #arxiv-2211.15199 #autotrain_compatible #endpoints_compatible #region-us 

# alephbertgimmel
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.

NOTE: This model was only trained with sequences of up to 128 tokens.

When using AlephBertGimmel, please reference:

Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 [URL



# alephbertgimmel
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.

NOTE: This model was only trained with sequences of up to 128 tokens.

When using AlephBertGimmel, please reference:

Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 [URL
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #arxiv-2211.15199 #autotrain_compatible #endpoints_compatible #region-us 
 # alephbertgimmel
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.

NOTE: This model was only trained with sequences of up to 128 tokens.

When using AlephBertGimmel, please reference:

Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 [URL
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #arxiv-2211.15199 #autotrain_compatible #endpoints_compatible #region-us 
# alephbertgimmel
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.

NOTE: This model was only trained with sequences of up to 128 tokens.

When using AlephBertGimmel, please reference:

Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 [URL