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--- |
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datasets: |
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- imdb (Movie corpus for Domain Adaptive Pretraining) |
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- cornell_movie_dialogue |
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- MIT Movie (NER Dataset) |
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language: |
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- English |
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thumbnail: |
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tags: |
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- roberta |
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- roberta-base |
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- token-classification |
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- NER |
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- named-entities |
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- BIO |
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- movies |
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- DAPT |
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license: cc-by-4.0 |
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--- |
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# Movie Roberta + Movies NER Task |
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Objective: |
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This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset |
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https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta. |
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``` |
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model_name = "thatdramebaazguy/movie-roberta-MITmovieroberta-base-MITmovie" |
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pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="ner") |
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``` |
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## Overview |
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**Language model:** roberta-base |
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**Language:** English |
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**Downstream-task:** NER |
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**Training data:** MIT Movie |
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**Eval data:** MIT Movie |
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**Infrastructure**: 2x Tesla v100 |
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**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh) |
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## Hyperparameters |
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``` |
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Num examples = 6253 |
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Num Epochs = 5 |
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Instantaneous batch size per device = 64 |
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Total train batch size (w. parallel, distributed & accumulation) = 128 |
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``` |
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## Performance |
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### Eval on MIT Movie |
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- epoch = 5.0 |
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- eval_accuracy = 0.9472 |
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- eval_f1 = 0.8876 |
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- eval_loss = 0.2211 |
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- eval_mem_cpu_alloc_delta = 3MB |
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- eval_mem_cpu_peaked_delta = 2MB |
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- eval_mem_gpu_alloc_delta = 0MB |
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- eval_mem_gpu_peaked_delta = 38MB |
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- eval_precision = 0.887 |
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- eval_recall = 0.8881 |
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- eval_runtime = 0:00:03.73 |
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- eval_samples = 1955 |
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- eval_samples_per_second = 523.095 |
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Github Repo: |
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- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) |
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--- |
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