catherinearnett
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B-GPT_pl_en_sequential

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----
-library_name: transformers
-tags: []
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+            ---
+            license: apache-2.0
+            datasets:
+            - oscar-corpus/OSCAR-2109
+            language:
+            - pl
+            - en
+            pipeline_tag: text-generation
+            library_name: transformers
+            ---
+# B-GPT_pl_en_sequential
+The B-GPT Models are bilingual GPT-2 style models. For the first half of training, this model was trained only on Polish data. In the second half of training, the model was trained on only {language_2} data.. At the end of training, 50 % of training data seen by the model is Polish and 50 % is English. The tokenizer was trained on the same proportions of Polish and English data.
+## Model details:
+    All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
+    For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
+    Details for this model specifically:
+    * Architecture: gpt2
+    * Parameters: 124770816
+    * Maximum sequence length: 512 tokens
+    * Training text data (raw): [XXXX]
+    * Training tokens: 12B
+    * Vocabulary size: 50000
+    * Compute cost: ~9 NVIDIA A6000 GPU hours
+    * CO2 Emission: 1.17 kg
+    Training datasets (percentages prior to deduplication):
+    * 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
+    Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.
+    ## Use This Model
+    Load the model:
+    ```
+    from transformers import AutoTokenizer, AutoModel
+    tokenizer = AutoTokenizer.from_pretrained("B-GPT_pl_en_sequential")
+    model = AutoModel.from_pretrained("B-GPT_pl_en_sequential")
+    ````
+    Text Generation:
+    ```
+    from transformers import pipeline
+    pipe = pipeline("text-generation", model="B-GPT_pl_en_sequential")
+    pipe("I am a")
+    ```
+    ## Citation
+    If you use this model, please cite:
+    ```
+    ```