File size: 1,960 Bytes
11cc824 7c55580 11cc824 81d42a0 7c55580 64364ea 7c55580 7455c26 7c55580 7455c26 7c55580 11cc824 7c55580 11cc824 7c55580 11cc824 7c55580 11cc824 7c55580 11cc824 7c55580 11cc824 7c55580 11cc824 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
tags: autonlp
language: en
widget:
- text: "Worry is a down payment on a problem you may never have'. Joyce Meyer. #motivation #leadership #worry"
datasets:
- tweet_eval
model-index:
- name: DistilBERT-tweet-eval-emotion
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name: "tweeteval"
type: tweet-eval
metrics:
- name: Accuracy
type: accuracy
value: 80.59
- name: Macro F1
type: macro-f1
value: 78.17
- name: Weighted F1
type: weighted-f1
value: 80.11
---
# `DistilBERT-tweet-eval-emotion` trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.5564454197883606
- Accuracy: 0.8057705840957072
- Macro F1: 0.7536021792986777
- Micro F1: 0.8057705840957073
- Weighted F1: 0.8011390170248318
- Macro Precision: 0.7817458823222652
- Micro Precision: 0.8057705840957072
- Weighted Precision: 0.8025156844840151
- Macro Recall: 0.7369154685020982
- Micro Recall: 0.8057705840957072
- Weighted Recall: 0.8057705840957072
## 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": "Worry is a down payment on a problem you may never have'. Joyce Meyer. #motivation #leadership #worry"}' https://api-inference.huggingface.co/models/philschmid/autonlp-tweet_eval_vs_comprehend-3092245
```
Or Python API:
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/DistilBERT-tweet-eval-emotion'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier("Worry is a down payment on a problem you may never have'. Joyce Meyer. #motivation #leadership #worry")
``` |