Instructions to use chungimungi/roberta-base-finetuned-math-science with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chungimungi/roberta-base-finetuned-math-science with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="chungimungi/roberta-base-finetuned-math-science")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("chungimungi/roberta-base-finetuned-math-science") model = AutoModelForMaskedLM.from_pretrained("chungimungi/roberta-base-finetuned-math-science", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
config.json
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{
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"architectures": [
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"RobertaForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.52.4",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3da5be8d4a2e3c381bdf2e3c0f1a1e2a67398959907be2580fcd9c11f43176c2
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size 498813948
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runs/Jun12_09-10-42_rbcdsaidgx/events.out.tfevents.1749699643.rbcdsaidgx.2945170.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:818284156f6b9fb039b9897f7b958315866a70037c04cec39519bca6304856fa
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size 17810
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d35e77177e3ec9620e348b5d5792ab00e688c9305660f0b66d0afdd9caa4e8df
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size 5368
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