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PooRaj
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span-marker-bert-base-fewnerd-coarse-super

Token Classification
SpanMarker
PyTorch
English
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Model card Files
xet
Community
PooRaj commited on
Commit
e4f6062
·
1 Parent(s): ee37e1e

End of training

Browse files
Files changed (3) hide show
  1. README.md +24 -24
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -51,13 +51,13 @@ model-index:
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  split: test
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  metrics:
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  - type: f1
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- value: 0.7715637979329663
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  name: F1
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  - type: precision
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- value: 0.7806880375293198
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  name: Precision
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  - type: recall
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- value: 0.7626503723505824
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  name: Recall
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  ---
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@@ -84,29 +84,29 @@ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained
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  ### Model Labels
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  | Label | Examples |
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  |:-------------|:-------------------------------------------------------------------------------|
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- | art | "Imelda de ' Lambertazzi", "The Seven Year Itch", "Time" |
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- | building | "Henry Ford Museum", "Boston Garden", "Sheremetyevo International Airport" |
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  | event | "French Revolution", "Iranian Constitutional Revolution", "Russian Revolution" |
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- | location | "the Republic of Croatia", "Croatian", "Mediterranean Basin" |
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- | organization | "Texas Chicken", "Church 's Chicken", "IAEA" |
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  | other | "N-terminal lipid", "BAR", "Amphiphysin" |
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- | person | "Ellaline Terriss", "Edmund Payne", "Hicks" |
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- | product | "Corvettes - GT1 C6R", "Phantom", "100EX" |
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  ## Evaluation
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  ### Metrics
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  | Label | Precision | Recall | F1 |
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  |:-------------|:----------|:-------|:-------|
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- | **all** | 0.7807 | 0.7627 | 0.7716 |
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- | art | 0.7548 | 0.7302 | 0.7423 |
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- | building | 0.6458 | 0.7143 | 0.6783 |
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- | event | 0.6211 | 0.5438 | 0.5799 |
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- | location | 0.8071 | 0.8535 | 0.8296 |
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- | organization | 0.7328 | 0.6631 | 0.6962 |
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- | other | 0.7560 | 0.6349 | 0.6902 |
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- | person | 0.8824 | 0.9016 | 0.8919 |
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- | product | 0.6784 | 0.5864 | 0.6291 |
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  ## Uses
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@@ -187,12 +187,12 @@ trainer.save_model("span_marker_model_id-finetuned")
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  ### Training Results
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  | Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
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  |:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
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- | 0.1629 | 200 | 0.0309 | 0.7609 | 0.5975 | 0.6693 | 0.9037 |
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- | 0.3259 | 400 | 0.0239 | 0.7449 | 0.7054 | 0.7246 | 0.9217 |
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- | 0.4888 | 600 | 0.0218 | 0.7598 | 0.7366 | 0.7480 | 0.9311 |
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- | 0.6517 | 800 | 0.0199 | 0.7863 | 0.7393 | 0.7621 | 0.9316 |
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- | 0.8147 | 1000 | 0.0195 | 0.7817 | 0.7575 | 0.7694 | 0.9379 |
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- | 0.9776 | 1200 | 0.0192 | 0.7778 | 0.7658 | 0.7717 | 0.9380 |
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  ### Framework Versions
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  - Python: 3.10.12
 
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  split: test
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  metrics:
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  - type: f1
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+ value: 0.7717265353418308
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  name: F1
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  - type: precision
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+ value: 0.7806212150810705
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  name: Precision
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  - type: recall
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+ value: 0.7630322703838075
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  name: Recall
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  ---
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  ### Model Labels
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  | Label | Examples |
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  |:-------------|:-------------------------------------------------------------------------------|
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+ | art | "Time", "The Seven Year Itch", "Imelda de ' Lambertazzi" |
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+ | building | "Boston Garden", "Sheremetyevo International Airport", "Henry Ford Museum" |
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  | event | "French Revolution", "Iranian Constitutional Revolution", "Russian Revolution" |
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+ | location | "Croatian", "the Republic of Croatia", "Mediterranean Basin" |
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+ | organization | "IAEA", "Texas Chicken", "Church 's Chicken" |
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  | other | "N-terminal lipid", "BAR", "Amphiphysin" |
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+ | person | "Hicks", "Edmund Payne", "Ellaline Terriss" |
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+ | product | "100EX", "Phantom", "Corvettes - GT1 C6R" |
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  ## Evaluation
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  ### Metrics
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  | Label | Precision | Recall | F1 |
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  |:-------------|:----------|:-------|:-------|
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+ | **all** | 0.7806 | 0.7630 | 0.7717 |
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+ | art | 0.7465 | 0.7395 | 0.7430 |
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+ | building | 0.6027 | 0.7184 | 0.6555 |
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+ | event | 0.6178 | 0.5438 | 0.5784 |
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+ | location | 0.8138 | 0.8547 | 0.8338 |
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+ | organization | 0.7359 | 0.6613 | 0.6966 |
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+ | other | 0.7397 | 0.6166 | 0.6726 |
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+ | person | 0.8845 | 0.9071 | 0.8957 |
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+ | product | 0.7056 | 0.5932 | 0.6446 |
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  ## Uses
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  ### Training Results
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  | Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
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  |:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
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+ | 0.1629 | 200 | 0.0359 | 0.6908 | 0.6298 | 0.6589 | 0.9053 |
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+ | 0.3259 | 400 | 0.0237 | 0.7535 | 0.7018 | 0.7267 | 0.9227 |
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+ | 0.4888 | 600 | 0.0216 | 0.7659 | 0.7438 | 0.7547 | 0.9333 |
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+ | 0.6517 | 800 | 0.0208 | 0.7730 | 0.7550 | 0.7639 | 0.9344 |
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+ | 0.8147 | 1000 | 0.0197 | 0.7805 | 0.7567 | 0.7684 | 0.9372 |
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+ | 0.9776 | 1200 | 0.0194 | 0.7771 | 0.7634 | 0.7702 | 0.9381 |
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  ### Framework Versions
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  - Python: 3.10.12
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