| language: en | |
| license: cc-by-4.0 | |
| tags: | |
| - roberta | |
| - roberta-base | |
| - token-classification | |
| - NER | |
| - named-entities | |
| - BIO | |
| - movies | |
| datasets: | |
| - MIT Movie | |
| # roberta-base + Movies NER Task | |
| Objective: | |
| This is Roberta Base trained for the NER task using MIT Movie Dataset | |
| ``` | |
| model_name = "thatdramebaazguy/roberta-base-MITmovie" | |
| pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="ner") | |
| ``` | |
| ## Overview | |
| **Language model:** roberta-base | |
| **Language:** English | |
| **Downstream-task:** NER | |
| **Training data:** MIT Movie | |
| **Eval data:** MIT Movie | |
| **Infrastructure**: 2x Tesla v100 | |
| **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh) | |
| ## Hyperparameters | |
| ``` | |
| Num examples = 6253 | |
| Num Epochs = 5 | |
| Instantaneous batch size per device = 64 | |
| Total train batch size (w. parallel, distributed & accumulation) = 128 | |
| ``` | |
| ## Performance | |
| ### Eval on MIT Movie | |
| - epoch = 5.0 | |
| - eval_accuracy = 0.9476 | |
| - eval_f1 = 0.8853 | |
| - eval_loss = 0.2208 | |
| - eval_mem_cpu_alloc_delta = 17MB | |
| - eval_mem_cpu_peaked_delta = 2MB | |
| - eval_mem_gpu_alloc_delta = 0MB | |
| - eval_mem_gpu_peaked_delta = 38MB | |
| - eval_precision = 0.8833 | |
| - eval_recall = 0.8874 | |
| - eval_runtime = 0:00:03.62 | |
| - eval_samples = 1955 | |
| Github Repo: | |
| - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) | |
| --- | |