# 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/)

---

{"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "token-classification", "NER", "named-entities", "BIO", "movies"], "datasets": ["MIT Movie"]}
transformers pytorch tf jax roberta token-classification roberta-base NER named-entities BIO movies license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

# roberta-base + Movies NER Task

Objective:
  This is Roberta Base trained for the NER task using MIT Movie Dataset
  


## 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    

## Hyperparameters
 
## 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

---

# roberta-base + Movies NER Task

Objective:
  This is Roberta Base trained for the NER task using MIT Movie Dataset ## 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 ## Hyperparameters ## 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

---
TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base + Movies NER Task

Objective:
  This is Roberta Base trained for the NER task using MIT Movie Dataset ## 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 ## Hyperparameters ## 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

---
passage: TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base + Movies NER Task

Objective:
  This is Roberta Base trained for the NER task using MIT Movie Dataset## 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## Hyperparameters## 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

---