# MiniLM-L6-mnli-binary
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment". 
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.  

## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "MoritzLaurer/MiniLM-L6-mnli-binary"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "I liked the movie"
hypothesis = "The movie was good."

input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "not_entailment"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
[MultiNLI](https://huggingface.co/datasets/multi_nli).

### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
    num_train_epochs=5,              # total number of training epochs
    learning_rate=2e-05,
    per_device_train_batch_size=32,   # batch size per device during training
    per_device_eval_batch_size=32,    # batch size for evaluation
    warmup_ratio=0.1,                # number of warmup steps for learning rate scheduler
    weight_decay=0.06,               # strength of weight decay
    fp16=True                        # mixed precision training
)
```
### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886

## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases. 

### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. 
{"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I liked the movie. [SEP] The movie was good."}]}
transformers pytorch bert text-classification zero-shot-classification en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #zero-shot-classification #en #autotrain_compatible #endpoints_compatible #region-us 

# MiniLM-L6-mnli-binary
## Model description
This model was trained on the MultiNLI dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment". 
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.  

## Intended uses & limitations
#### How to use the model

### Training data
MultiNLI.

### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.

### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886

## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases. 

### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. 
# MiniLM-L6-mnli-binary ## Model description
This model was trained on the MultiNLI dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment". 
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models. ## Intended uses & limitations #### How to use the model ### Training data
MultiNLI. ### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters. ### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886 ## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases. ### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.
TAGS
#transformers #pytorch #bert #text-classification #zero-shot-classification #en #autotrain_compatible #endpoints_compatible #region-us 
 # MiniLM-L6-mnli-binary ## Model description
This model was trained on the MultiNLI dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment". 
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models. ## Intended uses & limitations #### How to use the model ### Training data
MultiNLI. ### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters. ### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886 ## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases. ### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.
passage: TAGS
#transformers #pytorch #bert #text-classification #zero-shot-classification #en #autotrain_compatible #endpoints_compatible #region-us 
# MiniLM-L6-mnli-binary## Model description
This model was trained on the MultiNLI dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment". 
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.## Intended uses & limitations#### How to use the model### Training data
MultiNLI.### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases.### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.