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Model: catherinearnett/B-GPT_es_en_simultaneous
Source: Original Platform

.gitattributes

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README.md

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+
+---
+license: apache-2.0
+datasets:
+- oscar-corpus/OSCAR-2109
+language:
+- es
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# B-GPT_es_en_simultaneous
+
+This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on Spanish data. In the second half of training, the model was trained on a 50%-50% mix of Spanish and English data. At the end of training, 75% of training data seen by the model is Spanish and 25% is English. The tokenizer was trained on the same overall proportions of data as the language model at the final step. 
+
+This model was released alongside the paper [On the Acquisition of Shared Grammatical Representations in Bilingual Language Models](https://arxiv.org/abs/2503.03962), which contains more details about the models. Additionally, the [OSF page](https://osf.io/5cw2e/) provides all code and data related to the project. 
+
+## 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 tokens: 12B
+* Vocabulary size: 50000
+* Compute cost: ~9 NVIDIA A6000 GPU hours
+* CO2 Emission: 1.17 kg
+
+Training dataset: [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:
+
+Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision.
+
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_nl_sequential")
+model = AutoModelForCausalLM.from_pretrained("catherinearnett/B-GPT_en_nl_sequential", revision = "128000")
+```
+
+Text Generation:
+
+```
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_nl_sequential")
+    
+print(pipe("I am a", max_length=20)[0]["generated_text"])
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@article{arnett2025acquisition,
+  title={On the Acquisition of Shared Grammatical Representations in Bilingual Language Models},
+  author={Arnett, Catherine and Chang, Tyler A and Michaelov, James A and Bergen, Benjamin K},
+  journal={arXiv preprint arXiv:2503.03962},
+  year={2025}
+}
+```
+

config.json

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+{
+  "_name_or_path": "es_en_7525/model/checkpoint-128000",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50000,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50001,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 128,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 128,
+  "pad_token_id": 50002,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "use_cache": true,
+  "vocab_size": 51200
+}

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