second version

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 es_pharmaconer_ner_trf-3.4.0-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
 transformer/model filter=lfs diff=lfs merge=lfs -text
+es_pharmaconer_ner_trf-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,18 +1,10 @@
 ---
-
-language:
-- es
-
 tags:
-- biomedical
-- clinical
-- eHR
 - spacy
 - token-classification
-
-license: apache-2.0
-
-
+language:
+- es
+license: mit
 model-index:
 - name: es_pharmaconer_ner_trf
   results:
@@ -22,112 +14,46 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.0.9109481404
+      value: 0.9066736184
     - name: NER Recall
       type: recall
       value: 0.9152631579
     - name: NER F Score
       type: f_score
       value: 0.9109481404
-      
-widget:
-- text: "Se realizó estudio analítico destacando incremento de niveles de PTH y vitamina D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al exceso de suplementación de vitamina D."
-- text: "Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio en nuestras consultas, realizándose análisis con función renal, calcio sérico y urinario, calcio iónico, magnesio y PTH, que fueron normales."
-- text: "Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos antinucleares (ANA) y serologías, examen de orina, así como biopsia de la lesión. Los resultados fueron normales, con ANA, anti-Sm, anti-RNP, anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70 negativos."
-
 ---
-# 
+Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the  [official website](https://temu.bsc.es/pharmaconer/). Visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL
+
+| Feature | Description |
+| --- | --- |
+| **Name** | `es_pharmaconer_ner_trf` |
+| **Version** | `3.4.1` |
+| **spaCy** | `>=3.4.1,<3.5.0` |
+| **Default Pipeline** | `transformer`, `ner` |
+| **Components** | `transformer`, `ner` |
+| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
+| **Sources** | n/a |
+| **License** | `mit` |
+| **Author** | [The Text Mining Unit from Barcelona Supercomputing Center.](https://huggingface.co/PlanTL-GOB-ES/) |
+
+### Label Scheme
 
-## Table of Contents
 <details>
-<summary>Click to expand</summary>
-
-- [Overview](#overview)
-- [Model description](#model-description)
-- [How to use](#how-to-use)
-- [Intended uses and limitations](#intended-uses-and-limitations)
-- [Training](#training)
-  - [Training data](#training-data)
-  - [Training procedure](#training-procedure)
-- [Evaluation](#evaluation)
-   - [Evaluation results](#evaluation-results)
-- [Additional information](#additional-information)
-  - [Contact information](#contact-information)
-  - [Copyright](#copyright)
-  - [Licensing information](#licensing-information)
-  - [Funding](#funding)
-  - [Citation information](#citation-information)
-  - [Disclaimer](#disclaimer)
-
-</details>
-
-## Overview
-- **Architecture:** 
-- **Language:** 
-- **Task:** 
-- **Data:** 
-
-## Model description
-Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the  [official website](https://temu.bsc.es/pharmaconer/). Visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL",
-  "author":"The Text Mining Unit from Barcelona Supercomputing Center.
-
-
-## Intended uses and limitations
-
-
-## How to use
-
-
-## Limitations and bias
-
 
-## Training
+<summary>View label scheme (4 labels for 1 components)</summary>
 
-### Training data 
+| Component | Labels |
+| --- | --- |
+| **`ner`** | `NORMALIZABLES`, `NO_NORMALIZABLES`, `PROTEINAS`, `UNCLEAR` |
 
-### Training procedure
-
-
-## Evaluation
-
-### Evaluation results
-
-
-## Additional information
-
-### Author
-Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
-
-### Contact information
-For further information, send an email to <plantl-gob-es@bsc.es>
-
-### Copyright
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
-
-### Licensing information
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
-
-### Funding
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
-
-
-### Citation Information
-
-### Disclaimer
-
-<details>
-<summary>Click to expand</summary>
-
-The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
-
-When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
-
-In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
-
-
-Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
+</details>
 
-Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
+### Accuracy
 
-En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
-</details>
+| Type | Score |
+| --- | --- |
+| `ENTS_F` | 91.09 |
+| `ENTS_P` | 90.67 |
+| `ENTS_R` | 91.53 |
+| `TRANSFORMER_LOSS` | 15719.51 |
+| `NER_LOSS` | 22469.88 |

es_pharmaconer_ner_trf-any-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed5f08f4df81f25c5f007e59d0b8bd98541f6e0c13572aa347b87858690a9ca9
+size 440440035

meta.json

@@ -1,20 +1,19 @@
 {
   "lang":"es",
   "name":"pharmaconer_ner_trf",
-  "version":"3.4.0",
-  "spacy_version":">=3.4.1,<3.5.0",
+  "version":"3.4.1",
   "description":"Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the  [official website](https://temu.bsc.es/pharmaconer/). Visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL",
   "author":"The Text Mining Unit from Barcelona Supercomputing Center.",
   "email":"plantl-gob-es@bsc.es",
   "url":"https://huggingface.co/PlanTL-GOB-ES/",
-  "license":"Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)",
+  "license":"mit",
+  "spacy_version":">=3.4.1,<3.5.0",
   "spacy_git_version":"Unknown",
   "vectors":{
     "width":0,
     "vectors":0,
     "keys":0,
-    "name":null,
-    "mode":"default"
+    "name":null
   },
   "labels":{
     "transformer":[
@@ -66,5 +65,8 @@
     },
     "transformer_loss":157.1950596615,
     "ner_loss":224.6988260417
-  }
-}
+  },
+  "requirements":[
+    "spacy-transformers>=1.1.8,<1.2.0"
+  ]
+}