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# distilroberta-base-finetuned-amazon-products

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the on 1 million sentences sampled randomly from the pool of approximately 20 million product titles, product descriptions and product bullet points extracted out of Amazon India Scrapped Dataset in 2023. The model is evaluated on 0.1 million sentences which were held out.
It achieves the following results on the evaluation set:
- Loss: 2.1609
- Perplexity: 8.68

## Usage

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline

model_checkpoint = "RishiDarkDevil/distilroberta-base-finetuned-amazon-products"

tokenizer = AutoTokenizer.from_pretrained("distilroberta-base", use_fast=True)
model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states = True)

fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)

fill_mask('Made with Super Hard Coating and Shock <mask> technology and it protects the display') 
# actual word: Absorption, predicted: 'proof' (0.36), 'absorption' (0.23)
```

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.5129        | 1.0   | 125000 | 2.3504          |
| 2.3418        | 2.0   | 250000 | 2.2269          |
| 2.2978        | 3.0   | 375000 | 2.1161          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-amazon-products", "results": []}]}
transformers pytorch tensorboard roberta fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilroberta-base-finetuned-amazon-products
============================================


This model is a fine-tuned version of distilroberta-base on the on 1 million sentences sampled randomly from the pool of approximately 20 million product titles, product descriptions and product bullet points extracted out of Amazon India Scrapped Dataset in 2023. The model is evaluated on 0.1 million sentences which were held out.
It achieves the following results on the evaluation set:


* Loss: 2.1609
* Perplexity: 8.68


Usage
-----


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3