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Upload folder using huggingface_hub

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  1. README.md +9 -9
README.md CHANGED
@@ -1,7 +1,7 @@
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  ---
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  language:
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- - en
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- - eng
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  inference: false
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  tags:
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  - BERT
@@ -11,7 +11,7 @@ tags:
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  license: apache-2.0
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  ---
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- # HPLT v4.0 GPT-BERT for English
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  <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
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@@ -38,10 +38,10 @@ from transformers import AutoTokenizer, AutoModelForMaskedLM
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  # Import model
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  tokenizer = AutoTokenizer.from_pretrained(
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- "HPLT/hplt_gpt_bert_base_4_0_eng_Latn",
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  )
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  model = AutoModelForMaskedLM.from_pretrained(
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- "HPLT/hplt_gpt_bert_base_4_0_eng_Latn",
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  trust_remote_code=True,
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  use_safetensors=False,
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  )
@@ -77,10 +77,10 @@ import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  tokenizer = AutoTokenizer.from_pretrained(
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- "HPLT/hplt_gpt_bert_base_4_0_eng_Latn",
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  )
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  model = AutoModelForCausalLM.from_pretrained(
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- "HPLT/hplt_gpt_bert_base_4_0_eng_Latn",
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  trust_remote_code=True,
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  use_safetensors=False,
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  )
@@ -117,13 +117,13 @@ We are releasing 10 intermediate checkpoints for each model at intervals of ever
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  You can load a specific model revision with `transformers` using the argument `revision`:
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  ```python
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- model = AutoModelForSeq2SeqLM.from_pretrained("HPLT/hplt_gpt_bert_base_4_0_eng_Latn", revision="step21875", trust_remote_code=True)
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  ```
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  You can access all the revisions for the models with the following code:
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  ```python
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  from huggingface_hub import list_repo_refs
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- out = list_repo_refs("HPLT/hplt_gpt_bert_base_4_0_eng_Latn")
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  print([b.name for b in out.branches])
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  ```
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  ---
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  language:
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+ - bs
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+ - bos
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  inference: false
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  tags:
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  - BERT
 
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  license: apache-2.0
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  ---
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+ # HPLT v4.0 GPT-BERT for Bosnian
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  <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
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  # Import model
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  tokenizer = AutoTokenizer.from_pretrained(
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+ "HPLT/hplt_gpt_bert_base_4_0_bos_Latn",
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  )
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  model = AutoModelForMaskedLM.from_pretrained(
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+ "HPLT/hplt_gpt_bert_base_4_0_bos_Latn",
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  trust_remote_code=True,
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  use_safetensors=False,
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  )
 
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  tokenizer = AutoTokenizer.from_pretrained(
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+ "HPLT/hplt_gpt_bert_base_4_0_bos_Latn",
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  )
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  model = AutoModelForCausalLM.from_pretrained(
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+ "HPLT/hplt_gpt_bert_base_4_0_bos_Latn",
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  trust_remote_code=True,
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  use_safetensors=False,
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  )
 
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  You can load a specific model revision with `transformers` using the argument `revision`:
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  ```python
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+ model = AutoModelForSeq2SeqLM.from_pretrained("HPLT/hplt_gpt_bert_base_4_0_bos_Latn", revision="step21875", trust_remote_code=True)
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  ```
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  You can access all the revisions for the models with the following code:
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  ```python
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  from huggingface_hub import list_repo_refs
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+ out = list_repo_refs("HPLT/hplt_gpt_bert_base_4_0_bos_Latn")
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  print([b.name for b in out.branches])
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  ```
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