---
title: autotrain-predictiveoutcomes_transcripts-65215136105
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/autotrain-predictiveoutcomes_transcripts-65215136105"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/autotrain-predictiveoutcomes_transcripts-65215136105.md"
repository: AI-ModelScope/autotrain-predictiveoutcomes_transcripts-65215136105
last_updated: 2024-11-01
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - distilbert
architectures:
  - DistilBertForSequenceClassification
parameters: 65.8M
tensor_type:
  - F32
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 1837
stars: 0
tags:
  - autotrain
  - text-classification
---

# autotrain-predictiveoutcomes_transcripts-65215136105

> autotrain-predictiveoutcomes_transcripts-65215136105 - AI-ModelScope 在 ModelScope 开源的模型。Model Trained Using AutoTrain

- **Repository**: AI-ModelScope/autotrain-predictiveoutcomes_transcripts-65215136105
- **Tasks**: text-classification
- **Parameters**: 65.8M
- **Tags**: autotrain, text-classification
- **Downloads**: 1837
- **Stars**: 0
- **Last updated**: 2024-11-01

Source: https://www.modelscope.cn/models/AI-ModelScope/autotrain-predictiveoutcomes_transcripts-65215136105

---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65215136105
- CO2 Emissions (in grams): 0.3308

## Validation Metrics

- Loss: 0.787
- Accuracy: 0.733
- Macro F1: 0.121
- Micro F1: 0.733
- Weighted F1: 0.620
- Macro Precision: 0.105
- Micro Precision: 0.733
- Weighted Precision: 0.537
- Macro Recall: 0.143
- Micro Recall: 0.733
- Weighted Recall: 0.733


## 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-predictiveoutcomes_transcripts-65215136105
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MarketingHHM/autotrain-predictiveoutcomes_transcripts-65215136105", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MarketingHHM/autotrain-predictiveoutcomes_transcripts-65215136105", use_auth_token=True)

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

outputs = model(**inputs)
```