EMBO
/

sd-smallmol-roles

@@ -1,86 +1,84 @@
----
-language:
-- english
-thumbnail:
-tags:
-- token classification
-license: agpl-3.0
-datasets:
-- EMBO/sd-nlp
-metrics:
--
----
-# sd-smallmol-roles
-## Model description
-This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It has then been fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities.
-## Intended uses & limitations
-#### How to use
-The intended use of this model is to infer the semantic role of small molecules with regard to the causal hypotheses tested in experiments reported in scientific papers.
-To have a quick check of the model:
-```python
-from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
-example = """<s>The <mask> overexpression in cells caused an increase in <mask> expression.</s>"""
-tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
-model = RobertaForTokenClassification.from_pretrained('EMBO/sd-smallmol-roles')
-ner = pipeline('ner', model, tokenizer=tokenizer)
-res = ner(example)
-for r in res:
-    print(r['word'], r['entity'])
-```
-#### Limitations and bias
-The model must be used with the `roberta-base` tokenizer.
-## Training data
-The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) which includes manually annotated examples.
-## Training procedure
-The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
-Training code is available at https://github.com/source-data/soda-roberta
-- Model fine tuned: EMBL/bio-lm
-- Tokenizer vocab size: 50265
-- Training data: EMBO/sd-nlp
-- Dataset configuration: SMALL_MOL_ROLES
-- Training with 48771 examples.
-- Evaluating on 13801 examples.
-- Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR
-- Epochs: 0.33
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `learning_rate`: 0.0001
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-## Eval results
-On 7178 example of test set with `sklearn.metrics`:
-```
-                precision    recall  f1-score   support
-CONTROLLED_VAR       0.76      0.90      0.83      2946
-  MEASURED_VAR       0.60      0.71      0.65       852
-     micro avg       0.73      0.86      0.79      3798
-     macro avg       0.68      0.80      0.74      3798
-  weighted avg       0.73      0.86      0.79      3798
-{'test_loss': 0.011743436567485332, 'test_accuracy_score': 0.9951612532624371, 'test_precision': 0.7261345852895149, 'test_recall': 0.8551869404949973, 'test_f1': 0.7853947527505744, 'test_runtime': 58.0378, 'test_samples_per_second': 123.678, 'test_steps_per_second': 1.947}
-```

+---
+language: en
+license: agpl-3.0
+tags:
+- token classification
+datasets:
+- EMBO/sd-nlp
+metrics: []
+---
+# sd-smallmol-roles
+## Model description
+This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It has then been fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities.
+## Intended uses & limitations
+#### How to use
+The intended use of this model is to infer the semantic role of small molecules with regard to the causal hypotheses tested in experiments reported in scientific papers.
+To have a quick check of the model:
+```python
+from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
+example = """<s>The <mask> overexpression in cells caused an increase in <mask> expression.</s>"""
+tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
+model = RobertaForTokenClassification.from_pretrained('EMBO/sd-smallmol-roles')
+ner = pipeline('ner', model, tokenizer=tokenizer)
+res = ner(example)
+for r in res:
+    print(r['word'], r['entity'])
+```
+#### Limitations and bias
+The model must be used with the `roberta-base` tokenizer.
+## Training data
+The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) which includes manually annotated examples.
+## Training procedure
+The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
+Training code is available at https://github.com/source-data/soda-roberta
+- Model fine tuned: EMBL/bio-lm
+- Tokenizer vocab size: 50265
+- Training data: EMBO/sd-nlp
+- Dataset configuration: SMALL_MOL_ROLES
+- Training with 48771 examples.
+- Evaluating on 13801 examples.
+- Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR
+- Epochs: 0.33
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 0.0001
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+## Eval results
+On 7178 example of test set with `sklearn.metrics`:
+```
+                precision    recall  f1-score   support
+CONTROLLED_VAR       0.76      0.90      0.83      2946
+  MEASURED_VAR       0.60      0.71      0.65       852
+     micro avg       0.73      0.86      0.79      3798
+     macro avg       0.68      0.80      0.74      3798
+  weighted avg       0.73      0.86      0.79      3798
+{'test_loss': 0.011743436567485332, 'test_accuracy_score': 0.9951612532624371, 'test_precision': 0.7261345852895149, 'test_recall': 0.8551869404949973, 'test_f1': 0.7853947527505744, 'test_runtime': 58.0378, 'test_samples_per_second': 123.678, 'test_steps_per_second': 1.947}
+```