# Targeted Sentiment Analysis in News Headlines

BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.

(details to be published)


## Examples

Input is as follows

`Headline [SEP] Target`

where headline is the news title and target is an entity present in the headline.

Try

`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri` (should be NEG)

and

`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández` (POS or NEU)

{}
transformers pytorch safetensors bert text-classification autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 

# Targeted Sentiment Analysis in News Headlines

BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.

(details to be published)


## Examples

Input is as follows

'Headline [SEP] Target'

where headline is the news title and target is an entity present in the headline.

Try

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri' (should be NEG)

and

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández' (POS or NEU)

# Targeted Sentiment Analysis in News Headlines

BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.

(details to be published) ## Examples

Input is as follows

'Headline [SEP] Target'

where headline is the news title and target is an entity present in the headline.

Try

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri' (should be NEG)

and

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández' (POS or NEU)
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Targeted Sentiment Analysis in News Headlines

BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.

(details to be published) ## Examples

Input is as follows

'Headline [SEP] Target'

where headline is the news title and target is an entity present in the headline.

Try

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri' (should be NEG)

and

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández' (POS or NEU)
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Targeted Sentiment Analysis in News Headlines

BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.

(details to be published)## Examples

Input is as follows

'Headline [SEP] Target'

where headline is the news title and target is an entity present in the headline.

Try

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri' (should be NEG)

and

'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández' (POS or NEU)