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Merge pull request #449 from allenai/051_upgrade
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0.5.1 (spacy 3.4.x) upgrade
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@dakinggg
dakinggg authored Sep 7, 2022
2 parents 57fe1b3 + 40ded23 commit e30b8f4
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2 changes: 1 addition & 1 deletion Dockerfile
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Expand Up @@ -18,7 +18,7 @@ WORKDIR /work
COPY requirements.in .

RUN pip install -r requirements.in
RUN pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz
RUN pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_sm-0.5.1.tar.gz
RUN python -m spacy download en_core_web_sm
RUN python -m spacy download en_core_web_md

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18 changes: 9 additions & 9 deletions README.md
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Expand Up @@ -19,7 +19,7 @@ pip install scispacy
to install a model (see our full selection of available models below), run a command like the following:

```bash
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_sm-0.5.1.tar.gz
```

Note: We strongly recommend that you use an isolated Python environment (such as virtualenv or conda) to install scispacy.
Expand Down Expand Up @@ -76,14 +76,14 @@ pip install CMD-V(to paste the copied URL)

| Model | Description | Install URL
|:---------------|:------------------|:----------|
| en_core_sci_sm | A full spaCy pipeline for biomedical data with a ~100k vocabulary. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz)|
| en_core_sci_md | A full spaCy pipeline for biomedical data with a ~360k vocabulary and 50k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_md-0.5.0.tar.gz)|
| en_core_sci_lg | A full spaCy pipeline for biomedical data with a ~785k vocabulary and 600k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_lg-0.5.0.tar.gz)|
| en_core_sci_scibert | A full spaCy pipeline for biomedical data with a ~785k vocabulary and `allenai/scibert-base` as the transformer model. You may want to [use a GPU](https://spacy.io/usage#gpu) with this model. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_scibert-0.5.0.tar.gz)|
| en_ner_craft_md| A spaCy NER model trained on the CRAFT corpus.|[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_craft_md-0.5.0.tar.gz)|
| en_ner_jnlpba_md | A spaCy NER model trained on the JNLPBA corpus.| [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_jnlpba_md-0.5.0.tar.gz)|
| en_ner_bc5cdr_md | A spaCy NER model trained on the BC5CDR corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_bc5cdr_md-0.5.0.tar.gz)|
| en_ner_bionlp13cg_md | A spaCy NER model trained on the BIONLP13CG corpus. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_bionlp13cg_md-0.5.0.tar.gz)|
| en_core_sci_sm | A full spaCy pipeline for biomedical data with a ~100k vocabulary. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_sm-0.5.1.tar.gz)|
| en_core_sci_md | A full spaCy pipeline for biomedical data with a ~360k vocabulary and 50k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_md-0.5.1.tar.gz)|
| en_core_sci_lg | A full spaCy pipeline for biomedical data with a ~785k vocabulary and 600k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_lg-0.5.1.tar.gz)|
| en_core_sci_scibert | A full spaCy pipeline for biomedical data with a ~785k vocabulary and `allenai/scibert-base` as the transformer model. You may want to [use a GPU](https://spacy.io/usage#gpu) with this model. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_scibert-0.5.1.tar.gz)|
| en_ner_craft_md| A spaCy NER model trained on the CRAFT corpus.|[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_craft_md-0.5.1.tar.gz)|
| en_ner_jnlpba_md | A spaCy NER model trained on the JNLPBA corpus.| [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_jnlpba_md-0.5.1.tar.gz)|
| en_ner_bc5cdr_md | A spaCy NER model trained on the BC5CDR corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_bc5cdr_md-0.5.1.tar.gz)|
| en_ner_bionlp13cg_md | A spaCy NER model trained on the BIONLP13CG corpus. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_bionlp13cg_md-0.5.1.tar.gz)|


## Additional Pipeline Components
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3 changes: 2 additions & 1 deletion configs/base_parser_tagger.cfg
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Expand Up @@ -55,8 +55,9 @@ upstream = "*"
factory = "tagger"

[components.tagger.model]
@architectures = "spacy.Tagger.v1"
@architectures = "spacy.Tagger.v2"
nO = null
normalize = False

[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
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32 changes: 16 additions & 16 deletions docs/index.md
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Expand Up @@ -17,14 +17,14 @@ pip install <Model URL>

| Model | Description | Install URL
|:---------------|:------------------|:----------|
| en_core_sci_sm | A full spaCy pipeline for biomedical data. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz)|
| en_core_sci_md | A full spaCy pipeline for biomedical data with a larger vocabulary and 50k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_md-0.5.0.tar.gz)|
| en_core_sci_scibert | A full spaCy pipeline for biomedical data with a ~785k vocabulary and `allenai/scibert-base` as the transformer model. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_scibert-0.5.0.tar.gz)|
| en_core_sci_lg | A full spaCy pipeline for biomedical data with a larger vocabulary and 600k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_lg-0.5.0.tar.gz)|
| en_ner_craft_md| A spaCy NER model trained on the CRAFT corpus.|[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_craft_md-0.5.0.tar.gz)|
| en_ner_jnlpba_md | A spaCy NER model trained on the JNLPBA corpus.| [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_jnlpba_md-0.5.0.tar.gz)|
| en_ner_bc5cdr_md | A spaCy NER model trained on the BC5CDR corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_bc5cdr_md-0.5.0.tar.gz)|
| en_ner_bionlp13cg_md | A spaCy NER model trained on the BIONLP13CG corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_ner_bionlp13cg_md-0.5.0.tar.gz)|
| en_core_sci_sm | A full spaCy pipeline for biomedical data. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_sm-0.5.1.tar.gz)|
| en_core_sci_md | A full spaCy pipeline for biomedical data with a larger vocabulary and 50k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_md-0.5.1.tar.gz)|
| en_core_sci_scibert | A full spaCy pipeline for biomedical data with a ~785k vocabulary and `allenai/scibert-base` as the transformer model. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_scibert-0.5.1.tar.gz)|
| en_core_sci_lg | A full spaCy pipeline for biomedical data with a larger vocabulary and 600k word vectors. |[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_lg-0.5.1.tar.gz)|
| en_ner_craft_md| A spaCy NER model trained on the CRAFT corpus.|[Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_craft_md-0.5.1.tar.gz)|
| en_ner_jnlpba_md | A spaCy NER model trained on the JNLPBA corpus.| [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_jnlpba_md-0.5.1.tar.gz)|
| en_ner_bc5cdr_md | A spaCy NER model trained on the BC5CDR corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_bc5cdr_md-0.5.1.tar.gz)|
| en_ner_bionlp13cg_md | A spaCy NER model trained on the BIONLP13CG corpus. | [Download](https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_bionlp13cg_md-0.5.1.tar.gz)|



Expand All @@ -34,18 +34,18 @@ Our models achieve performance within 3% of published state of the art dependenc

| model | UAS | LAS | POS | Mentions (F1) | Web UAS |
|:---------------|:----|:------|:------|:---|:---|
| en_core_sci_sm | 89.27| 87.33 | 98.29 | 68.05 | 87.61 |
| en_core_sci_md | 89.86| 87.92 | 98.43 | 69.32 | 88.05 |
| en_core_sci_lg | 89.54| 87.66 | 98.29 | 69.52 | 87.68 |
| en_core_sci_scibert | 92.28| 90.83 | 98.93 | 67.84 | 92.63 |
| en_core_sci_sm | 89.03| 87.00 | 98.13 | 67.87 | 87.42 |
| en_core_sci_md | 89.73| 87.85 | 98.40 | 69.53 | 87.79 |
| en_core_sci_lg | 89.75| 87.79 | 98.49 | 69.69 | 87.74 |
| en_core_sci_scibert | 92.21| 90.65 | 98.86 | 68.01 | 92.58 |


| model | F1 | Entity Types|
|:---------------|:-----|:--------|
| en_ner_craft_md | 78.35|GGP, SO, TAXON, CHEBI, GO, CL|
| en_ner_jnlpba_md | 70.89| DNA, CELL_TYPE, CELL_LINE, RNA, PROTEIN |
| en_ner_bc5cdr_md | 84.70| DISEASE, CHEMICAL|
| en_ner_bionlp13cg_md | 76.79| AMINO_ACID, ANATOMICAL_SYSTEM, CANCER, CELL, CELLULAR_COMPONENT, DEVELOPING_ANATOMICAL_STRUCTURE, GENE_OR_GENE_PRODUCT, IMMATERIAL_ANATOMICAL_ENTITY, MULTI-TISSUE_STRUCTURE, ORGAN, ORGANISM, ORGANISM_SUBDIVISION, ORGANISM_SUBSTANCE, PATHOLOGICAL_FORMATION, SIMPLE_CHEMICAL, TISSUE |
| en_ner_craft_md | 76.75|GGP, SO, TAXON, CHEBI, GO, CL|
| en_ner_jnlpba_md | 72.28| DNA, CELL_TYPE, CELL_LINE, RNA, PROTEIN |
| en_ner_bc5cdr_md | 84.53| DISEASE, CHEMICAL|
| en_ner_bionlp13cg_md | 76.57| AMINO_ACID, ANATOMICAL_SYSTEM, CANCER, CELL, CELLULAR_COMPONENT, DEVELOPING_ANATOMICAL_STRUCTURE, GENE_OR_GENE_PRODUCT, IMMATERIAL_ANATOMICAL_ENTITY, MULTI-TISSUE_STRUCTURE, ORGAN, ORGANISM, ORGANISM_SUBDIVISION, ORGANISM_SUBSTANCE, PATHOLOGICAL_FORMATION, SIMPLE_CHEMICAL, TISSUE |


### Example Usage
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