
Fine-tuned T5 small model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet](https://framenet2.icsi.berkeley.edu/).


### Usage
This is meant to be used a part of [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer). See that project for usage instructions.

### Tasks

This model is trained to perform 3 tasks related to semantic frame parsing:
1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence

### Performance

This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by [Open Sesame](https://github.com/swabhs/open-sesame). 

| Task                   | F1 Score (Dev) | F1 Score (Test) |
| ---------------------- | -------------- | --------------- |
| Trigger identification | 0.75           | 0.71           |
| Frame Classification   | 0.87           | 0.86           |
| Argument Extraction    | 0.76           | 0.73           |
{"license": "apache-2.0"}
transformers pytorch t5 text2text-generation license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Fine-tuned T5 small model for use as a frame semantic parser in the Frame Semantic Transformer project. This model is trained on data from FrameNet.


### Usage


This is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions.


### Tasks


This model is trained to perform 3 tasks related to semantic frame parsing:


1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence


### Performance


This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by Open Sesame.


Task: Trigger identification, F1 Score (Dev): 0.75, F1 Score (Test): 0.71
Task: Frame Classification, F1 Score (Dev): 0.87, F1 Score (Test): 0.86
Task: Argument Extraction, F1 Score (Dev): 0.76, F1 Score (Test): 0.73



### Usage


This is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions. ### Tasks


This model is trained to perform 3 tasks related to semantic frame parsing:


1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence ### Performance


This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by Open Sesame.


Task: Trigger identification, F1 Score (Dev): 0.75, F1 Score (Test): 0.71
Task: Frame Classification, F1 Score (Dev): 0.87, F1 Score (Test): 0.86
Task: Argument Extraction, F1 Score (Dev): 0.76, F1 Score (Test): 0.73
TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Usage


This is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions. ### Tasks


This model is trained to perform 3 tasks related to semantic frame parsing:


1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence ### Performance


This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by Open Sesame.


Task: Trigger identification, F1 Score (Dev): 0.75, F1 Score (Test): 0.71
Task: Frame Classification, F1 Score (Dev): 0.87, F1 Score (Test): 0.86
Task: Argument Extraction, F1 Score (Dev): 0.76, F1 Score (Test): 0.73
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Usage


This is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions.### Tasks


This model is trained to perform 3 tasks related to semantic frame parsing:


1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence### Performance


This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by Open Sesame.


Task: Trigger identification, F1 Score (Dev): 0.75, F1 Score (Test): 0.71
Task: Frame Classification, F1 Score (Dev): 0.87, F1 Score (Test): 0.86
Task: Argument Extraction, F1 Score (Dev): 0.76, F1 Score (Test): 0.73