---
license: apache-2.0
datasets:
- imdb
language:
- en
metrics:
- f1
- accuracy
- recall
- precision
library_name: peft
---
# A Finetuned Bloom 1b1 Model for Sequence Classification

<!-- Provide a quick summary of what the model is/does. -->

The model was developed as a personal learning experience to fine tune a ready language model for Text Classification and to use it 
on real life data from the internet to perform sentiment analysis.

It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

The model achieves the following scores on the evaluation set during the fine tuning:

![Screenshot 2024-01-03 at 16.08.46.png](https://cdn-uploads.huggingface.co/production/uploads/64857e2b745fb671250a5beb/26EB2jJDKI0gsnvjHA9WP.png)

Here is the train/ eval/ test split:

```
DatasetDict({
    train: Dataset({
        features: ['review', 'sentiment'],
        num_rows: 36000
    })
    test: Dataset({
        features: ['review', 'sentiment'],
        num_rows: 5000
    })
    eval: Dataset({
        features: ['review', 'sentiment'],
        num_rows: 9000
    })
})
```

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Snoop088
- **Model type:** Text Classification / Sequence Classification
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model: bigscience/bloom-1b1

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/snoop088/imdb_tuned-bloom1b1-sentiment-classifier/tree/main
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

The model is intended to be used for Text Classification.

### Direct Use

Example script to use the model. Please note that this is peft adapter on the Bloom 1b model:

```
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
model_name = 'snoop088/imdb_tuned-bloom1b1-sentiment-classifier'
loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name, 
                                                                  trust_remote_code=True, 
                                                                  num_labels=2,
                                                                  device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

my_set = pd.read_csv("./data/df_manual.csv")

inputs = tokenizer(list(my_set["review"]), truncation=True, padding="max_length", max_length=256,  return_tensors="pt").to(DEVICE)
outputs = loaded_model(**inputs)
outcome = np.argmax(torch.Tensor.cpu(outputs.logits), axis=-1)

```

[More Information Needed]

### Downstream Use [optional]

The purpose of this model is to be used to perform sentiment analysis on a dataset similar to the one by IMDB. It should work well on product reviews, too in my opinion.


[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Training is done on the IMDB dataset available on the Hub:

[imdb](https://huggingface.co/datasets/imdb)

[More Information Needed]

### Training Procedure 

```
training_arguments = TrainingArguments(
    output_dir="your_tuned_model_name",
    save_strategy="epoch",
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=4,
    optim="adamw_torch",
    evaluation_strategy="steps",
    logging_steps=5,
    learning_rate=1e-5,
    max_grad_norm = 0.3,
    eval_steps=0.2,
    num_train_epochs=2,
    warmup_ratio= 0.1,
    # group_by_length=True,
    fp16=False,
    weight_decay=0.001,
    lr_scheduler_type="constant",
)

peft_model = get_peft_model(model, LoraConfig(
                            task_type="SEQ_CLS",
                            r=16,
                            lora_alpha=16,
                            target_modules=[
                                'query_key_value',
                                'dense'
                            ],
                            bias="none",
                            lora_dropout=0.05, # Conventional
                        ))

```
LORA results in: trainable params: 3,542,016 || all params: 1,068,859,392 || trainable%: 0.3313827830405592

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

Simple preprocessing with DataCollator:

```
def process_data(example):
    item = tokenizer(example["review"], truncation=True, max_length=320) # see if this is OK for dyn padding
    item["labels"] = [ 1 if sent == 'positive' else 0 for sent in example["sentiment"]]
    return item

tokenised_data = tokenised_data.remove_columns(["review", "sentiment"])
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
```


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
Evaluation function:
```
import evaluate

def compute_metrics(eval_pred):
    # All metrics are already predefined in the HF `evaluate` package
    precision_metric = evaluate.load("precision")
    recall_metric = evaluate.load("recall")
    f1_metric= evaluate.load("f1")
    accuracy_metric = evaluate.load("accuracy")

    logits, labels = eval_pred # eval_pred is the tuple of predictions and labels returned by the model
    predictions = np.argmax(logits, axis=-1)
    precision = precision_metric.compute(predictions=predictions, references=labels)["precision"]
    recall = recall_metric.compute(predictions=predictions, references=labels)["recall"]
    f1 = f1_metric.compute(predictions=predictions, references=labels)["f1"]
    accuracy = accuracy_metric.compute(predictions=predictions, references=labels)["accuracy"]
    # The trainer is expecting a dictionary where the keys are the metrics names and the values are the scores. 
    return {"precision": precision, "recall": recall, "f1-score": f1, 'accuracy': accuracy}

```

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

- Model: 6.183.1 "13th Gen Intel(R) Core(TM) i9-13900K"
- GPU: Nvidia RTX 4900/ 24 GB
- Memory: 64 GB

#### Software

- python 3.11.6
- transformers 4.36.2
- torch 2.1.2
- peft 0.7.1
- numpy 1.26.2
- datasets 2.16.0

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]