File size: 2,115 Bytes
182734d a3bdc23 182734d 0eda60d fa73d2b 182734d f5a5ee9 182734d 8c7151f 182734d 0eda60d |
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 67 68 69 70 71 72 73 74 |
---
license: apache-2.0
base_model: Twitter/twhin-bert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: financial_twhin_bert_large_7labels
results: []
datasets:
- FinGPT/fingpt-sentiment-train
language:
- en
widget:
- text: "$KTOS: Kratos Defense and Security awarded a $39 million sole-source contract for Geolocation Global Support Service"
example_title: "Example 1"
- text: "$Google parent Alphabet Inc. reported revenue and earnings that fell short of analysts' expectations, showing the company's search advertising juggernaut was not immune to a slowdown in the digital ad market. The shares fell more than 6%."
example_title: "Example 2"
- text: "$LJPC - La Jolla Pharma to reassess development of LJPC-401"
example_title: "Example 3"
- text: "Watch $MARK over 43c in after-hours for continuation targeting the 50c area initially"
example title: "Example 4"
- text: "$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year"
example title: "Example 5"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# financial_twhin-bert-large-7labels
This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on 61K financial tweets.
It achieves the following results on the evaluation set:
- Loss: 0.7361
- Accuracy: 0.8946
- F1: 0.8969
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.284712783126724e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |