Adjustments Agreements Amendments Anti-Corruption Laws Applicable Laws Approvals Arbitration Assignments Assigns Authority Authorizations Base Salary Benefits Binding Effects Books Brokers Capitalization Change In Control Closings Compliance With Laws Confidentiality Consent To Jurisdiction Consents Construction Cooperation Costs Counterparts Death Defined Terms Definitions Disability Disclosures Duties Effective Dates Effectiveness Employment Enforceability Enforcements Entire Agreements Erisa Existence Expenses Fees Financial Statements Forfeitures Further Assurances General Governing Laws Headings Indemnifications Indemnity Insurances Integration Intellectual Property Interests Interpretations Jurisdictions Liens Litigations Miscellaneous Modifications No Conflicts No Defaults No Waivers Non-Disparagement Notices Organizations Participations Payments Positions Powers Publicity Qualifications Records Releases Remedies Representations Sales Sanctions Severability Solvency Specific Performance Submission To Jurisdiction Subsidiaries Successors Survival Tax Withholdings Taxes Terminations Terms Titles Transactions With Affiliates Use Of Proceeds Vacations Venues Vesting Waiver Of Jury Trials Waivers Warranties Withholdings
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Preetiha/autotrain-data-clause-classification
co2_eq_emissions: 44.494127975699804
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 812025458
- CO2 Emissions (in grams): 44.494127975699804

## Validation Metrics

- Loss: 0.5240132808685303
- Accuracy: 0.8673
- Macro F1: 0.7979496833221609
- Micro F1: 0.8673
- Weighted F1: 0.8616433030199793
- Macro Precision: 0.8263528446923423
- Micro Precision: 0.8673
- Weighted Precision: 0.8702574307362431
- Macro Recall: 0.7953048612545152
- Micro Recall: 0.8673
- Weighted Recall: 0.8673


## 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/Preetiha/autotrain-clause-classification-812025458
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Preetiha/autotrain-clause-classification-812025458", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Preetiha/autotrain-clause-classification-812025458", use_auth_token=True)

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

outputs = model(**inputs)
```