
## Validation Metrics

- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518744551003
- Micro Recall: 0.8263473053892215
- Weighted Recall: 0.8263473053892215


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."}' https://api-inference.huggingface.co/models/Shenzy/Sentence_Classification4DesignTutor
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

import numpy as np

labdic ={ 0: "rationale", 1: "suggestion", 2: "specific_critique"}

model = AutoModelForSequenceClassification.from_pretrained("Shenzy/Sentence_Classification4DesignTutor")

tokenizer = AutoTokenizer.from_pretrained("Shenzy/Sentence_Classification4DesignTutor")

inputs = tokenizer("An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name.", return_tensors="pt")

outputs = model(**inputs)

print(labdic[np.argmax(outputs)])
```
{"language": "en", "tags": "autotrain", "datasets": ["Shenzy/autotrain-data-sentence_classification"], "widget": [{"text": "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."}], "co2_eq_emissions": 0.00986494387043499}
transformers pytorch safetensors roberta text-classification autotrain en dataset:Shenzy/autotrain-data-sentence_classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #en #dataset-Shenzy/autotrain-data-sentence_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


## Validation Metrics

- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518744551003
- Micro Recall: 0.8263473053892215
- Weighted Recall: 0.8263473053892215


## Usage

You can use cURL to access this model:



Or Python API:


## Validation Metrics

- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518744551003
- Micro Recall: 0.8263473053892215
- Weighted Recall: 0.8263473053892215 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #en #dataset-Shenzy/autotrain-data-sentence_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 ## Validation Metrics

- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518744551003
- Micro Recall: 0.8263473053892215
- Weighted Recall: 0.8263473053892215 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #en #dataset-Shenzy/autotrain-data-sentence_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
## Validation Metrics

- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518744551003
- Micro Recall: 0.8263473053892215
- Weighted Recall: 0.8263473053892215## Usage

You can use cURL to access this model:



Or Python API: