---
title: autotrain-predictiveoutcomestranscripts-65459136117
canonical_url: "https://www.modelscope.ai/models/RealmSky/autotrain-predictiveoutcomestranscripts-65459136117"
md_url: "https://www.modelscope.ai/models/RealmSky/autotrain-predictiveoutcomestranscripts-65459136117.md"
repository: RealmSky/autotrain-predictiveoutcomestranscripts-65459136117
last_updated: 2026-01-12
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - bert
architectures:
  - BertForSequenceClassification
parameters: 108.3M
tensor_type:
  - F32
  - I64
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 6
stars: 0
tags:
  - autotrain
  - text-classification
---

# autotrain-predictiveoutcomestranscripts-65459136117

> autotrain-predictiveoutcomestranscripts-65459136117 - An open-source model by RealmSky on ModelScope. Model Trained Using AutoTrain

- **Repository**: RealmSky/autotrain-predictiveoutcomestranscripts-65459136117
- **Tasks**: text-classification
- **Parameters**: 108.3M
- **Tags**: autotrain, text-classification
- **Downloads**: 6
- **Stars**: 0
- **Last updated**: 2026-01-12

Source: https://www.modelscope.ai/models/RealmSky/autotrain-predictiveoutcomestranscripts-65459136117

---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65459136117
- CO2 Emissions (in grams): 1.8448

## Validation Metrics

- Loss: 1.543
- Accuracy: 0.409
- Macro F1: 0.218
- Micro F1: 0.409
- Weighted F1: 0.340
- Macro Precision: 0.194
- Micro Precision: 0.409
- Weighted Precision: 0.299
- Macro Recall: 0.260
- Micro Recall: 0.409
- Weighted Recall: 0.409


## 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": "I love AutoTrain"}' https://api-inference.huggingface.co/models/MarketingHHM/autotrain-predictiveoutcomestranscripts-65459136117
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MarketingHHM/autotrain-predictiveoutcomestranscripts-65459136117", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MarketingHHM/autotrain-predictiveoutcomestranscripts-65459136117", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```